implicit bias essay examples

Implicit Bias (Unconscious Bias): Definition & Examples

Charlotte Ruhl

Research Assistant & Psychology Graduate

BA (Hons) Psychology, Harvard University

Charlotte Ruhl, a psychology graduate from Harvard College, boasts over six years of research experience in clinical and social psychology. During her tenure at Harvard, she contributed to the Decision Science Lab, administering numerous studies in behavioral economics and social psychology.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

On This Page:

Implicit bias refers to the beliefs and attitudes that affect our understanding, actions and decisions in an unconscious way.

Take-home Messages

• Implicit biases are unconscious attitudes and stereotypes that can manifest in the criminal justice system, workplace, school setting, and in the healthcare system.
• Implicit bias is also known as unconscious bias or implicit social cognition.
• There are many different examples of implicit biases, ranging from categories of race, gender, and sexuality.
• These biases often arise from trying to find patterns and navigate the overwhelming stimuli in this complicated world. Culture, media, and upbringing can also contribute to the development of such biases.
• Removing these biases is a challenge, especially because we often don’t even know they exist, but research reveals potential interventions and provides hope that levels of implicit biases in the United States are decreasing.

The term implicit bias was first coined in 1995 by psychologists Mahzarin Banaji and Anthony Greenwald, who argued that social behavior is largely influenced by unconscious associations and judgments (Greenwald & Banaji, 1995).

So, what is implicit bias?

Specifically, implicit bias refers to attitudes or stereotypes that affect our understanding, actions, and decisions in an unconscious way, making them difficult to control.

Since the mid-90s, psychologists have extensively researched implicit biases, revealing that, without even knowing it, we all possess our own implicit biases.

System 1 and System 2 Thinking

Kahneman (2011) distinguishes between two types of thinking: system 1 and system 2.

• System 1 is the brain’s fast, emotional, unconscious thinking mode. This type of thinking requires little effort, but it is often error-prone. Most everyday activities (like driving, talking, cleaning, etc.) heavily use the type 1 system.
• System 2 is slow, logical, effortful, conscious thought, where reason dominates.

Implicit Bias vs. Explicit Bias

What is meant by implicit bias.

Implicit bias (unconscious bias) refers to attitudes and beliefs outside our conscious awareness and control. Implicit biases are an example of system one thinking, so we are unaware they exist (Greenwald & Krieger, 2006).

An implicit bias may counter a person’s conscious beliefs without realizing it. For example, it is possible to express explicit liking of a certain social group or approval of a certain action while simultaneously being biased against that group or action on an unconscious level.

Therefore, implicit and explicit biases might differ for the same person.

It is important to understand that implicit biases can become explicit biases. This occurs when you become consciously aware of your prejudices and beliefs. They surface in your mind, leading you to choose whether to act on or against them.

What is meant by explicit bias?

Explicit biases are biases we are aware of on a conscious level (for example, feeling threatened by another group and delivering hate speech as a result). They are an example of system 2 thinking.

It is also possible that your implicit and explicit biases differ from your neighbor, friend, or family member. Many factors can control how such biases are developed.

What Are the Implications of Unconscious Bias?

Implicit biases become evident in many different domains of society. On an interpersonal level, they can manifest in simply daily interactions.

This occurs when certain actions (or microaggressions) make others feel uncomfortable or aware of the specific prejudices you may hold against them.

Implicit Prejudice

Implicit prejudice is the automatic, unconscious attitudes or stereotypes that influence our understanding, actions, and decisions. Unlike explicit prejudice, which is consciously controlled, implicit prejudice can occur even in individuals who consciously reject prejudice and strive for impartiality.

Unconscious racial stereotypes are a major example of implicit prejudice. In other words, having an automatic preference for one race over another without being aware of this bias.

This bias can manifest in small interpersonal interactions and has broader implications in society’s legal system and many other important sectors.

Examples may include holding an implicit stereotype that associates Black individuals as violent. As a result, you may cross the street at night when you see a Black man walking in your direction without even realizing why you are crossing the street.

The action taken here is an example of a microaggression. A microaggression is a subtle, automatic, and often nonverbal that communicates hostile, derogatory, or negative prejudicial slights and insults toward any group (Pierce, 1970). Crossing the street communicates an implicit prejudice, even though you might not even be aware.

Another example of an implicit racial bias is if a Latino student is complimented by a teacher for speaking perfect English, but he is a native English speaker. Here, the teacher assumed that English would not be his first language simply because he is Latino.

Gender Stereotypes

Gender biases are another common form of implicit bias. Gender biases are the ways in which we judge men and women based on traditional feminine and masculine assigned traits.

For example, a greater assignment of fame to male than female names (Banaji & Greenwald, 1995) reveals a subconscious bias that holds men at a higher level than their female counterparts. Whether you voice the opinion that men are more famous than women is independent of this implicit gender bias.

Another common implicit gender bias regards women in STEM (science, technology, engineering, and mathematics).

In school, girls are more likely to be associated with language over math. In contrast, males are more likely to be associated with math over language (Steffens & Jelenec, 2011), revealing clear gender-related implicit biases that can ultimately go so far as to dictate future career paths.

Even if you outwardly say men and women are equally good at math, it is possible you subconsciously associate math more strongly with men without even being aware of this association.

Health Care

Healthcare is another setting where implicit biases are very present. Racial and ethnic minorities and women are subject to less accurate diagnoses, curtailed treatment options, less pain management, and worse clinical outcomes (Chapman, Kaatz, & Carnes, 2013).

Additionally, Black children are often not treated as children or given the same compassion or level of care provided for White children (Johnson et al., 2017).

It becomes evident that implicit biases infiltrate the most common sectors of society, making it all the more important to question how we can remove these biases.

LGBTQ+ Community Bias

Similar to implicit racial and gender biases, individuals may hold implicit biases against members of the LGBTQ+ community. Again, that does not necessarily mean that these opinions are voiced outwardly or even consciously recognized by the beholder, for that matter.

Rather, these biases are unconscious. A really simple example could be asking a female friend if she has a boyfriend, assuming her sexuality and that heterosexuality is the norm or default.

Instead, you could ask your friend if she is seeing someone in this specific situation. Several other forms of implicit biases fall into categories ranging from weight to ethnicity to ability that come into play in our everyday lives.

Legal System

Both law enforcement and the legal system shed light on implicit biases. An example of implicit bias functioning in law enforcement is the shooter bias – the tendency among the police to shoot Black civilians more often than White civilians, even when they are unarmed (Mekawi & Bresin, 2015).

This bias has been repeatedly tested in the laboratory setting, revealing an implicit bias against Black individuals. Blacks are also disproportionately arrested and given harsher sentences, and Black juveniles are tried as adults more often than their White peers.

Black boys are also seen as less childlike, less innocent, more culpable, more responsible for their actions, and as being more appropriate targets for police violence (Goff, 2014).

Together, these unconscious stereotypes, which are not rooted in truth, form an array of implicit biases that are extremely dangerous and utterly unjust.

Implicit biases are also visible in the workplace. One experiment that tracked the success of White and Black job applicants found that stereotypically White received 50% more callbacks than stereotypically Black names, regardless of the industry or occupation (Bertrand & Mullainathan, 2004).

This reveals another form of implicit bias: the hiring bias – Anglicized‐named applicants receiving more favorable pre‐interview impressions than other ethnic‐named applicants (Watson, Appiah, & Thornton, 2011).

We’re susceptible to bias because of these tendencies:

We tend to seek out patterns

A key reason we develop such biases is that our brains have a natural tendency to look for patterns and associations to make sense of a very complicated world.

Research shows that even before kindergarten, children already use their group membership (e.g., racial group, gender group, age group, etc.) to guide inferences about psychological and behavioral traits.

At such a young age, they have already begun seeking patterns and recognizing what distinguishes them from other groups (Baron, Dunham, Banaji, & Carey, 2014).

And not only do children recognize what sets them apart from other groups, they believe “what is similar to me is good, and what is different from me is bad” (Cameron, Alvarez, Ruble, & Fuligni, 2001).

Children aren’t just noticing how similar or dissimilar they are to others; dissimilar people are actively disliked (Aboud, 1988).

Recognizing what sets you apart from others and then forming negative opinions about those outgroups (a social group with which an individual does not identify) contributes to the development of implicit biases.

We like to take shortcuts

Another explanation is that the development of these biases is a result of the brain’s tendency to try to simplify the world.

Mental shortcuts make it faster and easier for the brain to sort through all of the overwhelming data and stimuli we are met with every second of the day. And we take mental shortcuts all the time. Rules of thumb, educated guesses, and using “common sense” are all forms of mental shortcuts.

Implicit bias is a result of taking one of these cognitive shortcuts inaccurately (Rynders, 2019). As a result, we incorrectly rely on these unconscious stereotypes to provide guidance in a very complex world.

And especially when we are under high levels of stress, we are more likely to rely on these biases than to examine all of the relevant, surrounding information (Wigboldus, Sherman, Franzese, & Knippenberg, 2004).

Social and Cultural influences

Influences from media, culture, and your individual upbringing can also contribute to the rise of implicit associations that people form about the members of social outgroups. Media has become increasingly accessible, and while that has many benefits, it can also lead to implicit biases.

The way TV portrays individuals or the language journal articles use can ingrain specific biases in our minds.

For example, they can lead us to associate Black people with criminals or females as nurses or teachers. The way you are raised can also play a huge role. One research study found that parental racial attitudes can influence children’s implicit prejudice (Sinclair, Dunn, & Lowery, 2005).

And parents are not the only figures who can influence such attitudes. Siblings, the school setting, and the culture in which you grow up can also shape your explicit beliefs and implicit biases.

Implicit Attitude Test (IAT)

What sets implicit biases apart from other forms is that they are subconscious – we don’t know if we have them.

However, researchers have developed the Implicit Association Test (IAT) tool to help reveal such biases.

The Implicit Attitude Test (IAT) is a psychological assessment to measure an individual’s unconscious biases and associations. The test measures how quickly a person associates concepts or groups (such as race or gender) with positive or negative attributes, revealing biases that may not be consciously acknowledged.

The IAT requires participants to categorize negative and positive words together with either images or words (Greenwald, McGhee, & Schwartz, 1998).

Tests are taken online and must be performed as quickly as possible, the faster you categorize certain words or faces of a category, the stronger the bias you hold about that category.

For example, the Race IAT requires participants to categorize White faces and Black faces and negative and positive words. The relative speed of association of black faces with negative words is used as an indication of the level of anti-black bias.

Professor Brian Nosek and colleagues tested more than 700,000 subjects. They found that more than 70% of White subjects more easily associated White faces with positive words and Black faces with negative words, concluding that this was evidence of implicit racial bias (Nosek, Greenwald, & Banaji, 2007).

Outside of lab testing, it is very difficult to know if we do, in fact, possess these biases. The fact that they are so hard to detect is in the very nature of this form of bias, making them very dangerous in various real-world settings.

How to Reduce Implicit Bias

Because of the harmful nature of implicit biases, it is critical to examine how we can begin to remove them.

Practicing mindfulness is one potential way, as it reduces the stress and cognitive load that otherwise leads to relying on such biases.

A 2016 study found that brief mediation decreased unconscious bias against black people and elderly people (Lueke & Gibson, 2016), providing initial insight into the usefulness of this approach and paving the way for future research on this intervention.

Adjust your perspective

Another method is perspective-taking – looking beyond your own point of view so that you can consider how someone else may think or feel about something.

Researcher Belinda Gutierrez implemented a videogame called “Fair Play,” in which players assume the role of a Black graduate student named Jamal Davis.

As Jamal, players experience subtle race bias while completing “quests” to obtain a science degree.

Gutierrez hypothesized that participants who were randomly assigned to play the game would have greater empathy for Jamal and lower implicit race bias than participants randomized to read narrative text (not perspective-taking) describing Jamal’s experience (Gutierrez, 2014), and her hypothesis was supported, illustrating the benefits of perspective taking in increasing empathy towards outgroup members.

Specific implicit bias training has been incorporated in different educational and law enforcement settings. Research has found that diversity training to overcome biases against women in STEM improved with men (Jackson, Hillard, & Schneider, 2014).

Training programs designed to target and help overcome implicit biases may also be beneficial for police officers (Plant & Peruche, 2005), but there is not enough conclusive evidence to completely support this claim. One pitfall of such training is a potential rebound effect.

Actively trying to inhibit stereotyping actually results in the bias eventually increasing more so than if it had not been initially suppressed in the first place (Macrae, Bodenhausen, Milne, & Jetten, 1994). This is very similar to the white bear problem that is discussed in many psychology curricula.

This concept refers to the psychological process whereby deliberate attempts to suppress certain thoughts make them more likely to surface (Wegner & Schneider, 2003).

Education is crucial. Understanding what implicit biases are, how they can arise how, and how to recognize them in yourself and others are all incredibly important in working towards overcoming such biases.

Learning about other cultures or outgroups and what language and behaviors may come off as offensive is critical as well. Education is a powerful tool that can extend beyond the classroom through books, media, and conversations.

On the bright side, implicit biases in the United States have been improving.

From 2007 to 2016, implicit biases have changed towards neutrality for sexual orientation, race, and skin-tone attitudes (Charlesworth & Banaji, 2019), demonstrating that it is possible to overcome these biases.

Books for further reading

As mentioned, education is extremely important. Here are a few places to get started in learning more about implicit biases:

• Biased: Uncovering the Hidden Prejudice That Shapes What We See Think and Do by Jennifer Eberhardt
• Blindspot by Anthony Greenwald and Mahzarin Banaji
• Implicit Racial Bias Across the Law by Justin Levinson and Robert Smith

Keywords and Terminology

To find materials on implicit bias and related topics, search databases and other tools using the following keywords:

Is unconscious bias the same as implicit bias?

Yes, unconscious bias is the same as implicit bias. Both terms refer to the biases we carry without awareness or conscious control, which can affect our attitudes and actions toward others.

In what ways can implicit bias impact our interactions with others?

Implicit bias can impact our interactions with others by unconsciously influencing our attitudes, behaviors, and decisions. This can lead to stereotyping, prejudice, and discrimination, even when we consciously believe in equality and fairness.

It can affect various domains of life, including workplace dynamics, healthcare provision, law enforcement, and everyday social interactions.

What are some implicit bias examples?

Some examples of implicit biases include assuming a woman is less competent than a man in a leadership role, associating certain ethnicities with criminal behavior, or believing that older people are not technologically savvy.

Other examples include perceiving individuals with disabilities as less capable or assuming that someone who is overweight is lazy or unmotivated.

Aboud, F. E. (1988). Children and prejudice . B. Blackwell.

Banaji, M. R., & Greenwald, A. G. (1995). Implicit gender stereotyping in judgments of fame. Journal of Personality and Social Psychology , 68 (2), 181.

Baron, A. S., Dunham, Y., Banaji, M., & Carey, S. (2014). Constraints on the acquisition of social category concepts. Journal of Cognition and Development , 15 (2), 238-268.

Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American economic review , 94 (4), 991-1013.

Cameron, J. A., Alvarez, J. M., Ruble, D. N., & Fuligni, A. J. (2001). Children’s lay theories about ingroups and outgroups: Reconceptualizing research on prejudice. Personality and Social Psychology Review , 5 (2), 118-128.

Chapman, E. N., Kaatz, A., & Carnes, M. (2013). Physicians and implicit bias: how doctors may unwittingly perpetuate health care disparities. Journal of general internal medicine , 28 (11), 1504-1510.

Charlesworth, T. E., & Banaji, M. R. (2019). Patterns of implicit and explicit attitudes: I. Long-term change and stability from 2007 to 2016. Psychological science , 30(2), 174-192.

Goff, P. A., Jackson, M. C., Di Leone, B. A. L., Culotta, C. M., & DiTomasso, N. A. (2014). The essence of innocence: consequences of dehumanizing Black children. Journal of personality and socialpsychology,106(4), 526.

Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: attitudes, self-esteem, and stereotypes. Psychological review, 102(1), 4.

Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. (1998). Measuring individual differences in implicit cognition: the implicit association test. Journal of personality and social psychology , 74(6), 1464.

Greenwald, A. G., & Krieger, L. H. (2006). Implicit bias: Scientific foundations. California Law Review , 94 (4), 945-967.

Gutierrez, B., Kaatz, A., Chu, S., Ramirez, D., Samson-Samuel, C., & Carnes, M. (2014). “Fair Play”: a videogame designed to address implicit race bias through active perspective taking. Games for health journal , 3 (6), 371-378.

Jackson, S. M., Hillard, A. L., & Schneider, T. R. (2014). Using implicit bias training to improve attitudes toward women in STEM. Social Psychology of Education , 17 (3), 419-438.

Johnson, T. J., Winger, D. G., Hickey, R. W., Switzer, G. E., Miller, E., Nguyen, M. B., … & Hausmann, L. R. (2017). Comparison of physician implicit racial bias toward adults versus children. Academic pediatrics , 17 (2), 120-126.

Kahneman, D. (2011). Thinking, fast and slow . Macmillan.

Lueke, A., & Gibson, B. (2016). Brief mindfulness meditation reduces discrimination. Psychology of Consciousness: Theory, Research, and Practice , 3 (1), 34.

Macrae, C. N., Bodenhausen, G. V., Milne, A. B., & Jetten, J. (1994). Out of mind but back in sight: Stereotypes on the rebound. Journal of personality and social psychology , 67 (5), 808.

Mekawi, Y., & Bresin, K. (2015). Is the evidence from racial bias shooting task studies a smoking gun? Results from a meta-analysis. Journal of Experimental Social Psychology , 61 , 120-130.

Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). The Implicit Association Test at age 7: A methodological and conceptual review. Automatic processes in social thinking and behavior , 4 , 265-292.

Pierce, C. (1970). Offensive mechanisms. The black seventies , 265-282.

Plant, E. A., & Peruche, B. M. (2005). The consequences of race for police officers’ responses to criminal suspects. Psychological Science , 16 (3), 180-183.

Rynders, D. (2019). Battling Implicit Bias in the IDEA to Advocate for African American Students with Disabilities. Touro L. Rev. , 35 , 461.

Sinclair, S., Dunn, E., & Lowery, B. (2005). The relationship between parental racial attitudes and children’s implicit prejudice. Journal of Experimental Social Psychology , 41 (3), 283-289.

Steffens, M. C., & Jelenec, P. (2011). Separating implicit gender stereotypes regarding math and language: Implicit ability stereotypes are self-serving for boys and men, but not for girls and women. Sex Roles , 64(5-6), 324-335.

Watson, S., Appiah, O., & Thornton, C. G. (2011). The effect of name on pre‐interview impressions and occupational stereotypes: the case of black sales job applicants. Journal of Applied Social Psychology , 41 (10), 2405-2420.

Wegner, D. M., & Schneider, D. J. (2003). The white bear story. Psychological Inquiry , 14 (3-4), 326-329.

Wigboldus, D. H., Sherman, J. W., Franzese, H. L., & Knippenberg, A. V. (2004). Capacity and comprehension: Spontaneous stereotyping under cognitive load. Social Cognition , 22 (3), 292-309.

Further Information

Test yourself for bias.

• Project Implicit (IAT Test) From Harvard University
• Implicit Association Test From the Social Psychology Network
• Test Yourself for Hidden Bias From Teaching Tolerance
• How The Concept Of Implicit Bias Came Into Being With Dr. Mahzarin Banaji, Harvard University. Author of Blindspot: hidden biases of good people5:28 minutes; includes a transcript
• Understanding Your Racial Biases With John Dovidio, Ph.D., Yale University From the American Psychological Association11:09 minutes; includes a transcript
• Talking Implicit Bias in Policing With Jack Glaser, Goldman School of Public Policy, University of California Berkeley21:59 minutes
• Implicit Bias: A Factor in Health Communication With Dr. Winston Wong, Kaiser Permanente19:58 minutes
• Bias, Black Lives and Academic Medicine Dr. David Ansell on Your Health Radio (August 1, 2015)21:42 minutes
• Uncovering Hidden Biases Google talk with Dr. Mahzarin Banaji, Harvard University
• Impact of Implicit Bias on the Justice System 9:14 minutes
• Students Speak Up: What Bias Means to Them 2:17 minutes
• Weight Bias in Health Care From Yale University16:56 minutes
• Gender and Racial Bias In Facial Recognition Technology 4:43 minutes

Journal Articles

• An implicit bias primer Mitchell, G. (2018). An implicit bias primer. Virginia Journal of Social Policy & the Law , 25, 27–59.
• Implicit Association Test at age 7: A methodological and conceptual review Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). The Implicit Association Test at age 7: A methodological and conceptual review. Automatic processes in social thinking and behavior, 4 , 265-292.
• Implicit Racial/Ethnic Bias Among Health Care Professionals and Its Influence on Health Care Outcomes: A Systematic Review Hall, W. J., Chapman, M. V., Lee, K. M., Merino, Y. M., Thomas, T. W., Payne, B. K., … & Coyne-Beasley, T. (2015). Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: a systematic review. American Journal of public health, 105 (12), e60-e76.
• Reducing Racial Bias Among Health Care Providers: Lessons from Social-Cognitive Psychology Burgess, D., Van Ryn, M., Dovidio, J., & Saha, S. (2007). Reducing racial bias among health care providers: lessons from social-cognitive psychology. Journal of general internal medicine, 22 (6), 882-887.
• Integrating implicit bias into counselor education Boysen, G. A. (2010). Integrating Implicit Bias Into Counselor Education. Counselor Education & Supervision, 49 (4), 210–227.
• Cognitive Biases and Errors as Cause—and Journalistic Best Practices as Effect Christian, S. (2013). Cognitive Biases and Errors as Cause—and Journalistic Best Practices as Effect. Journal of Mass Media Ethics, 28 (3), 160–174.
• Empathy intervention to reduce implicit bias in pre-service teachers Whitford, D. K., & Emerson, A. M. (2019). Empathy Intervention to Reduce Implicit Bias in Pre-Service Teachers. Psychological Reports, 122 (2), 670–688.

Related Articles

Cognitive Psychology

Automatic Processing in Psychology: Definition & Examples

Controlled Processing in Psychology: Definition & Examples

How Ego Depletion Can Drain Your Willpower

What is the Default Mode Network?

Theories of Selective Attention in Psychology

Availability Heuristic and Decision Making

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

How Does Implicit Bias Influence Behavior?

Strategies to Reduce the Impact of Implicit Bias

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Akeem Marsh, MD, is a board-certified child, adolescent, and adult psychiatrist who has dedicated his career to working with medically underserved communities.

Getty Images 

• Measurement
• Discrimination

An implicit bias is an unconscious association, belief, or attitude toward any social group. Implicit biases are one reason why people often attribute certain qualities or characteristics to all members of a particular group, a phenomenon known as stereotyping .

It is important to remember that implicit biases operate almost entirely on an unconscious level . While explicit biases and prejudices are intentional and controllable, implicit biases are less so.

A person may even express explicit disapproval of a certain attitude or belief while still harboring similar biases on a more unconscious level. Such biases do not necessarily align with our own sense of self and personal identity. People can also hold positive or negative associations about their own race, gender, religion, sexuality, or other personal characteristics.

Causes of Implicit Bias

While people might like to believe that they are not susceptible to these implicit biases and stereotypes, the reality is that everyone engages in them whether they like it or not. This reality, however, does not mean that you are necessarily prejudiced or inclined to discriminate against other people. It simply means that your brain is working in a way that makes associations and generalizations.

In addition to the fact that we are influenced by our environment and stereotypes that already exist in the society into which we were born, it is generally impossible to separate ourselves from the influence of society.

You can, however, become more aware of your unconscious thinking and the ways in which society influences you.

It is the natural tendency of the brain to sift, sort, and categorize information about the world that leads to the formation of these implicit biases. We're susceptible to bias because of these tendencies:

• We tend to seek out patterns . Implicit bias occurs because of the brain's natural tendency to look for patterns and associations in the world. Social cognition , or our ability to store, process, and apply information about people in social situations, is dependent on this ability to form associations about the world.
• We like to take shortcuts . Like other cognitive biases , implicit bias is a result of the brain's tendency to try to simplify the world. Because the brain is constantly inundated with more information than it could conceivably process, mental shortcuts make it faster and easier for the brain to sort through all of this data.
• Our experiences and social conditioning play a role . Implicit biases are influenced by experiences, although these attitudes may not be the result of direct personal experience. Cultural conditioning, media portrayals, and upbringing can all contribute to the implicit associations that people form about the members of other social groups.

How Implicit Bias Is Measured

The term implicit bias was first coined by social psychologists Mahzarin Banaji and Tony Greenwald in 1995. In an influential paper introducing their theory of implicit social cognition, they proposed that social behavior was largely influenced by unconscious associations and judgments.

In 1998, Banaji and Greenwald published their now-famous Implicit Association Test (IAT) to support their hypothesis . The test utilizes a computer program to show respondents a series of images and words to determine how long it takes someone to choose between two things.

Subjects might be shown images of faces of different racial backgrounds, for example, in conjunction with either a positive word or a negative word. Subjects would then be asked to click on a positive word when they saw an image of someone from one race and to click on a negative word when they saw someone of another race.

Interpreting the Results

The researchers suggest that when someone clicks quickly, it means that they possess a stronger unconscious association.   If a person quickly clicks on a negative word every time they see a person of a particular race, the researchers suggest that this would indicate that they hold an implicit negative bias toward individuals of that race.

In addition to a test of implicit racial attitudes, the IAT has also been utilized to measure unconscious biases related to gender, weight, sexuality, disability, and other areas. The IAT has grown in popularity and use over the last decade, yet has recently come under fire.

Among the main criticisms are findings that the test results may lack reliability . Respondents may score high on racial bias on one test, and low the next time they are tested.

Also of concern is that scores on the test may not necessarily correlate with individual behavior. People may score high for a type of bias on the IAT, but those results may not accurately predict how they would relate to members of a specific social group.

Link Between Implicit Bias and Discrimination

It is important to understand that implicit bias is not the same thing as racism, although the two concepts are related. Overt racism involves conscious prejudice against members of a particular racial group and can be influenced by both explicit and implicit biases.

Other forms of discrimination that can be influenced by unconscious biases include ageism , sexism, homophobia, and ableism.

One of the benefits of being aware of the potential impact of implicit social biases is that you can take a more active role in overcoming social stereotypes, discrimination, and prejudice.

Effects of Implicit Bias

Implicit biases can influence how people behave toward the members of different social groups. Researchers have found that such bias can have effects in a number of settings, including in school, work, and legal proceedings.

Implicit Bias in School

Implicit bias can lead to a phenomenon known as stereotype threat in which people internalize negative stereotypes about themselves based upon group associations. Research has shown, for example, that young girls often internalize implicit attitudes related to gender and math performance.  

By the age of 9, girls have been shown to exhibit the unconscious beliefs that females have a preference for language over math.   The stronger these implicit beliefs are, the less likely girls and women are to pursue math performance in school. Such unconscious beliefs are also believed to play a role in inhibiting women from pursuing careers in science, technology, engineering, and mathematics (STEM) fields.

Studies have also demonstrated that implicit attitudes can also influence how teachers respond to student behavior, suggesting that implicit bias can have a powerful impact on educational access and academic achievement.

One study, for example, found that Black children—and Black boys in particular—were more likely to be expelled from school for behavioral issues. When teachers were told to watch for challenging behaviors, they were more likely to focus on Black children than on White children.

Implicit Bias In the Workplace

While the Implicit Attitude Test itself may have pitfalls, these problems do not negate the existence of implicit bias. Or the existence and effects of bias, prejudice, and discrimination in the real world. Such prejudices can have very real and potentially devastating consequences.

One study, for example, found that when Black and White job seekers sent out similar resumes to employers, Black applicants were half as likely to be called in for interviews as White job seekers with equal qualifications.

Such discrimination is likely the result of both explicit and implicit biases toward racial groups.

Even when employers strive to eliminate potential bias in hiring, subtle implicit biases may still have an impact on how people are selected for jobs or promoted to advanced positions. Avoiding such biases entirely can be difficult, but being aware of their existence and striving to minimize them can help.

Implicit Bias in Healthcare Settings

Certainly, age, race, or health condition should not play a role in how patients get treated, however, implicit bias can influence quality healthcare and have long-term impacts including suboptimal care, adverse outcomes, and even death.

For example, one study published in the American Journal of Public Health found that physicians with high scores in implicit bias tended to dominate conversations with Black patients and, as a result, the Black patients had less confidence and trust in the provider and rated the quality of their care lower.  

Researchers continue to investigate implicit bias in relation to other ethnic groups as well as specific health conditions, including type 2 diabetes, obesity, mental health, and substance use disorders.

Implicit Bias in Legal Settings

Implicit biases can also have troubling implications in legal proceedings, influencing everything from initial police contact all the way through sentencing. Research has found that there is an overwhelming racial disparity in how Black defendants are treated in criminal sentencing.  

Not only are Black defendants less likely to be offered plea bargains than White defendants charged with similar crimes, but they are also more likely to receive longer and harsher sentences than White defendants.

Strategies to Reduce the Impact of Implict Bias

Implicit biases impact behavior, but there are things that you can do to reduce your own bias. Some ways that you can reduce the influence of implicit bias:

• Focus on seeing people as individuals . Rather than focusing on stereotypes to define people, spend time considering them on a more personal, individual level.
• Work on consciously changing your stereotypes . If you do recognize that your response to a person might be rooted in biases or stereotypes, make an effort to consciously adjust your response.
• Take time to pause and reflect . In order to reduce reflexive reactions, take time to reflect on potential biases and replace them with positive examples of the stereotyped group. 
• Adjust your perspective . Try seeing things from another person's point of view. How would you respond if you were in the same position? What factors might contribute to how a person acts in a particular setting or situation?
• Increase your exposure . Spend more time with people of different racial backgrounds. Learn about their culture by attending community events or exhibits.
• Practice mindfulness . Try meditation, yoga, or focused breathing to increase mindfulness and become more aware of your thoughts and actions.

While implicit bias is difficult to eliminate altogether, there are strategies that you can utilize to reduce its impact. Taking steps such as actively working to overcome your biases , taking other people's perspectives, seeking greater diversity in your life, and building your awareness about your own thoughts are a few ways to reduce the impact of implicit bias.

A Word From Verywell

Implicit biases can be troubling, but they are also a pervasive part of life. Perhaps more troubling, your unconscious attitudes may not necessarily align with your declared beliefs. While people are more likely to hold implicit biases that favor their own in-group, it is not uncommon for people to hold biases against their own social group as well.

The good news is that these implicit biases are not set in stone. Even if you do hold unconscious biases against other groups of people, it is possible to adopt new attitudes, even on the unconscious level.   This process is not necessarily quick or easy, but being aware of the existence of these biases is a good place to start making a change.

Jost JT. The existence of implicit bias is beyond reasonable doubt: A refutation of ideological and methodological objections and executive summary of ten studies that no manager should ignore . Research in Organizational Behavior . 2009;29:39-69. doi:10.1016/j.riob.2009.10.001

Greenwald AG, Mcghee DE, Schwartz JL. Measuring individual differences in implicit cognition: The implicit association test . J Pers Soc Psychol. 1998;74(6):1464-1480. doi:10.1037/0022-3514.74.6.1464

Sabin J, Nosek BA, Greenwald A, Rivara FP. Physicians' implicit and explicit attitudes about race by MD race, ethnicity, and gender . J Health Care Poor Underserved. 2009;20(3):896-913. doi:10.1353/hpu.0.0185

Capers Q, Clinchot D, McDougle L, Greenwald AG. Implicit racial bias in medical school admissions . Acad Med . 2017;92(3):365-369. doi:10.1097/ACM.0000000000001388

Kiefer AK, Sekaquaptewa D. Implicit stereotypes and women's math performance: How implicit gender-math stereotypes influence women's susceptibility to stereotype threat .  Journal of Experimental Social Psychology. 2007;43(5):825-832. doi:10.1016/j.jesp.2006.08.004

Steffens MC, Jelenec P, Noack P. On the leaky math pipeline: Comparing implicit math-gender stereotypes and math withdrawal in female and male children and adolescents .  Journal of Educational Psychology. 2010;102(4):947-963. doi:10.1037/a0019920

Edward Zigler Center in Child Development & Social Policy, Yale School of Medicine. Implicit Bias in Preschool: A Research Study Brief .

Pager D, Western B, Bonikowski B. Discrimination in a low-wage labor market: A field experiment . Am Sociol Rev. 2009;74(5):777-799. doi:10.1177/000312240907400505

Malinen S, Johnston L. Workplace ageism: Discovering hidden bias . Exp Aging Res. 2013;39(4):445-465. doi:10.1080/0361073X.2013.808111

Cooper LA, Roter DL, Carson KA, et al. The associations of clinicians' implicit attitudes about race with medical visit communication and patient ratings of interpersonal care . Am J Public Health . 2012;102(5):979-87. doi:10.2105/AJPH.2011.300558

Leiber MJ, Fox KC. Race and the impact of detention on juvenile justice decision making .  Crime & Delinquency. 2005;51(4):470-497. doi:10.1177/0011128705275976

Van Ryn M, Hardeman R, Phelan SM, et al. Medical school experiences associated with change in implicit racial bias among 3547 students: A medical student CHANGES study report . J Gen Intern Med. 2015;30(12):1748-1756. doi:10.1007/s11606-015-3447-7

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Featured Topics

Featured series.

A series of random questions answered by Harvard experts.

Explore the Gazette

Read the latest.

Footnote leads to exploration of start of for-profit prisons in N.Y.

Should NATO step up role in Russia-Ukraine war?

It’s on Facebook, and it’s complicated

Mahzarin Banaji opened the symposium on Tuesday by recounting the “implicit association” experiments she had done at Yale and at Harvard. The final talk is today at 9 a.m.

Kris Snibbe/Harvard Staff Photographer

Turning a light on our implicit biases

Brett Milano

Harvard Correspondent

Social psychologist details research at University-wide faculty seminar

Few people would readily admit that they’re biased when it comes to race, gender, age, class, or nationality. But virtually all of us have such biases, even if we aren’t consciously aware of them, according to Mahzarin Banaji, Cabot Professor of Social Ethics in the Department of Psychology, who studies implicit biases. The trick is figuring out what they are so that we can interfere with their influence on our behavior.

Banaji was the featured speaker at an online seminar Tuesday, “Blindspot: Hidden Biases of Good People,” which was also the title of Banaji’s 2013 book, written with Anthony Greenwald. The presentation was part of Harvard’s first-ever University-wide faculty seminar.

“Precipitated in part by the national reckoning over race, in the wake of George Floyd, Breonna Taylor and others, the phrase ‘implicit bias’ has almost become a household word,” said moderator Judith Singer, Harvard’s senior vice provost for faculty development and diversity. Owing to the high interest on campus, Banaji was slated to present her talk on three different occasions, with the final one at 9 a.m. Thursday.

Banaji opened on Tuesday by recounting the “implicit association” experiments she had done at Yale and at Harvard. The assumptions underlying the research on implicit bias derive from well-established theories of learning and memory and the empirical results are derived from tasks that have their roots in experimental psychology and neuroscience. Banaji’s first experiments found, not surprisingly, that New Englanders associated good things with the Red Sox and bad things with the Yankees.

She then went further by replacing the sports teams with gay and straight, thin and fat, and Black and white. The responses were sometimes surprising: Shown a group of white and Asian faces, a test group at Yale associated the former more with American symbols though all the images were of U.S. citizens. In a further study, the faces of American-born celebrities of Asian descent were associated as less American than those of white celebrities who were in fact European. “This shows how discrepant our implicit bias is from even factual information,” she said.

How can an institution that is almost 400 years old not reveal a history of biases, Banaji said, citing President Charles Eliot’s words on Dexter Gate: “Depart to serve better thy country and thy kind” and asking the audience to think about what he may have meant by the last two words.

She cited Harvard’s current admission strategy of seeking geographic and economic diversity as examples of clear progress — if, as she said, “we are truly interested in bringing the best to Harvard.” She added, “We take these actions consciously, not because they are easy but  because they are in our interest and in the interest of society.”

Moving beyond racial issues, Banaji suggested that we sometimes see only what we believe we should see. To illustrate she showed a video clip of a basketball game and asked the audience to count the number of passes between players. Then the psychologist pointed out that something else had occurred in the video — a woman with an umbrella had walked through — but most watchers failed to register it. “You watch the video with a set of expectations, one of which is that a woman with an umbrella will not walk through a basketball game. When the data contradicts an expectation, the data doesn’t always win.”

Expectations, based on experience, may create associations such as “Valley Girl Uptalk” is the equivalent of “not too bright.” But when a quirky way of speaking spreads to a large number of young people from certain generations,  it stops being a useful guide. And yet, Banaji said, she has been caught in her dismissal of a great idea presented in uptalk.  Banaji stressed that the appropriate course of action is not to ask the person to change the way she speaks but rather for her and other decision makers to know that using language and accents to judge ideas is something people at their own peril.

Banaji closed the talk with a personal story that showed how subtler biases work: She’d once turned down an interview because she had issues with the magazine for which the journalist worked.

The writer accepted this and mentioned she’d been at Yale when Banaji taught there. The professor then surprised herself by agreeing to the interview based on this fragment of shared history that ought not to have influenced her. She urged her colleagues to think about positive actions, such as helping that perpetuate the status quo.

“You and I don’t discriminate the way our ancestors did,” she said. “We don’t go around hurting people who are not members of our own group. We do it in a very civilized way: We discriminate by who we help. The question we should be asking is, ‘Where is my help landing? Is it landing on the most deserved, or just on the one I shared a ZIP code with for four years?’”

To subscribe to short educational modules that help to combat implicit biases, visit outsmartinghumanminds.org .

Share this article

You might like.

Historian traces 19th-century murder case that brought together historical figures, helped shape American thinking on race, violence, incarceration

National security analysts outline stakes ahead of July summit

‘Spermworld’ documentary examines motivations of prospective parents, volunteer donors who connect through private group page 

Epic science inside a cubic millimeter of brain

Researchers publish largest-ever dataset of neural connections

Finding right mix on campus speech policies

Legal, political scholars discuss balancing personal safety, constitutional rights, academic freedom amid roiling protests, cultural shifts

Good genes are nice, but joy is better

Harvard study, almost 80 years old, has proved that embracing community helps us live longer, and be happier

• Table of Contents
• Random Entry
• Chronological
• Editorial Information
• About the SEP
• Editorial Board
• How to Cite the SEP
• Special Characters
• Advanced Tools
• Support the SEP
• PDFs for SEP Friends
• Make a Donation
• SEPIA for Libraries
• Entry Contents

Bibliography

Academic tools.

• Friends PDF Preview
• Author and Citation Info
• Back to Top

Implicit Bias

Research on “implicit bias” suggests that people can act on the basis of prejudice and stereotypes without intending to do so. While psychologists in the field of “implicit social cognition” study consumer products, self-esteem, food, alcohol, political values, and more, the most striking and well-known research has focused on implicit biases toward members of socially stigmatized groups, such as African-Americans, women, and the LGBTQ community. [ 1 ] For example, imagine Frank, who explicitly believes that women and men are equally suited for careers outside the home. Despite his explicitly egalitarian belief, Frank might nevertheless behave in any number of biased ways, from distrusting feedback from female co-workers to hiring equally qualified men over women. Part of the reason for Frank’s discriminatory behavior might be an implicit gender bias. Psychological research on implicit bias has grown steadily (§1), raising metaphysical (§2), epistemological (§3), and ethical questions (§4). [ 2 ]

1.1 History of the Field

1.2 implicit measures, 2.1 attitudes, 2.2 implicit processes, 2.3.1 the propositional model of implicit attitudes, 2.3.2 generic belief, 2.3.3 spinozan belief fixation, 2.5 situations, 3.1 self-knowledge, 3.2.1 perceptual belief, 3.2.2 global skepticism, 3.3 ethical/epistemic dilemmas, 4.1.1 arguments from awareness, 4.1.2 arguments from control, 4.1.3 attributionism and deep self theories, 4.2.1 change-based interventions, 4.2.2 control-based interventions, 5.2 implicit vs. explicit, 5.3 predicting behavior, 5.4 structuralism, 6. future research, manuscripts, other links, related entries, 1. introduction: history and measures of implicit social cognition.

While Allport’s (1954) The Nature of Prejudice remains a touchstone for psychological research on prejudice, the study of implicit social cognition has two distinct and more recent sets of roots. [ 3 ] The first stems from the distinction between “controlled” and “automatic” information processing made by cognitive psychologists in the 1970s (e.g., Shiffrin & Schneider 1977). While controlled processing was thought to be voluntary, attention-demanding, and of limited capacity, automatic processing was thought to unfold without attention, to have nearly unlimited capacity, and to be hard to suppress voluntarily (Payne & Gawronski 2010; see also Bargh 1994). In important early work on implicit cognition, Fazio and colleagues showed that attitudes can be understood as activated by either controlled or automatic processes. In Fazio’s (1995) “sequential priming” task, for example, following exposure to social group labels (e.g., “black”, “women”, etc.), subjects’ reaction times (or “response latencies”) to stereotypic words (e.g., “lazy” or “nurturing”) are measured. People respond more quickly to concepts closely linked together in memory, and most subjects in the sequential priming task are quicker to respond to words like “lazy” following exposure to “black” than “white”. Researchers standardly take this pattern to indicate a prejudiced automatic association between semantic concepts. The broader notion embedded in this research was that subjects’ automatic responses were thought to be “uncontaminated” by controlled or strategic responses (Amodio & Devine 2009).

While this first stream of research focused on automaticity, a second stream focused on (un)consciousness. Many studies demonstrated that awareness of stereotypes can affect social judgment and behavior in relative independence from subjects’ reported attitudes (Devine 1989; Devine & Monteith 1999; Dovidio & Gaertner 2004; Greenwald & Banaji 1995; Banaji et al. 1993). These studies were influenced by theories of implicit memory (e.g., Jacoby & Dallas 1981; Schacter 1987), leading to Greenwald & Banaji’s original definition of “implicit attitudes” as

introspectively unidentified (or inaccurately identified) traces of past experience that mediate favorable or unfavorable feeling, thought, or action toward social objects. (1995: 8)

The guiding idea here, as Dovidio and Gaertner (1986) put it, is that in the modern world prejudice has been “driven underground,” that is, out of conscious awareness. This idea has led to the common view that what makes a bias implicit is that a person is unwilling or unable to report it. Recent findings have challenged this view, however (§3.1)

What a person says is not necessarily a good representation of the whole of what she feels and thinks, nor of how she will behave. Arguably, the central advance of research on implicit social cognition is the ability to assess people’s thoughts, feelings, and behavior without having to ask them directly, “what do you think/feel about X?” or “what would you do in X situation?”

Implicit measures, then, might be thought of as instruments that assess people’s thoughts, feelings, and behavior indirectly, that is, without relying on “self-report.” This is too quick, however. For example, a survey that asks “what do you think of black people” is explicit and direct, in the sense that the subject’s judgment is both explicitly reported and the subject is being directly asked about the topic of interest to the researchers. However, a survey that asks “what do you think about Darnell” (i.e., a person with a stereotypically black name) is explicit and indirect, because the subject’s judgment is explicitly reported but the content of what is being judged (i.e., the subject’s attitudes toward race) is inferred by the researcher. The distinction between direct and indirect measures is also relative rather than absolute. Even in some direct measures, such as personality inventories, subjects may not be completely aware of what is being studied.

In the literature, “implicit” is used to refer to at least four distinct things (Gawronski & Brannon 2017): (1) a distinctive psychological construct, such as an “implicit attitude,” which is assessed by a variety of instruments; (2) a family of instruments, called “implicit measures,” that assess people’s thoughts and feelings in a specific way (e.g., in a way that minimizes subjects’ reliance on introspection and their ability to respond strategically); (3) a set of cognitive and affective processes—“implicit processes”—that affect responses on a variety of measures; and (4) a kind of evaluative behavior—e.g., a categorization judgment—elicited by specific circumstances, such as cognitive load. In this entry, I will use “implicit” in the senses of (2) and (4), unless otherwise noted. One virtue of this approach is that it allows one to remain agnostic about the nature of the phenomena implicit measures assess. [ 4 ] Consider Frank again. His implicit gender bias may be assessed by several different instruments, such as sequential priming or the “Implicit Association Test” (IAT; Greenwald et al. 1998). The IAT—the most well-known implicit test—is a reaction time measure. In a standard IAT, the subject attempts to sort words or pictures into categories as fast as possible while making as few errors as possible. In the images below, the correct answers would be left, right, left, right.

All images are copyright of Project Implicit and reproduced here with permission.

An IAT score is computed by comparing speed and error rates on the “blocks” (or trials) in which the pairing of concepts is consistent with common stereotypes (images 1 and 3) to the blocks in which the pairing of the concepts is inconsistent with common stereotypes (images 2 and 4). If he is typical of most subjects, Frank will be faster and make fewer errors on stereotype-consistent trials than stereotype-inconsistent trials. While this “gender-career” IAT pairs concepts (e.g., “male” and “career”), other IATs, such as the “race-evaluation” IAT, pair a concept to an evaluation (e.g., “black” and “bad”). Other IATs assess body image, age, sexual orientation, and so on. As of 2019, approximately 26 million IATs have been taken (although it is unclear if this number represents 26 million unique participants or 26 million tests taken or started; Lai p.c.). One review (Nosek et al. 2007), which tested over 700,000 subjects on the race-evaluation IAT, found that over 70% of white participants more easily associated black faces with negative words (e.g., war, bad) and white faces with positive words (e.g., peace, good). The researchers consider this an implicit preference for white faces over black faces. [ 5 ]

Although the IAT remains the most popular implicit measure, it is far from the only one. Other prominent implicit measures, many of which are derivations of sequential priming, are semantic priming (Banaji & Hardin 1996) and the Affect Misattribution Procedure (AMP; Payne et al. 2005). Also, a “second generation” of categorization-based measures (like the IAT) has been developed. For example, the Go/No-go Association Task (GNAT; Nosek & Banaji 2001) presents subjects with one target object rather than two in order to determine whether preferences or aversions are primarily responsible for scores on the standard IAT (i.e., the ease of pairing good words with white faces and bad words with black faces, or the difficulty of pairing good words with black faces and bad words with white faces; Brewer 1999).

A notable advance in the psychometrics of implicit bias has been the advent of multinomial (or formal process) models, which identify distinct processes contributing to performance on implicit measures. For example, elderly people tend to show greater bias on the race-evaluation IAT compared with younger people, but this may be due to their having stronger preferences for whites or having weaker control over their biased responding (Nosek et al. 2011). Multinomial models, like the Quadruple Process Model (Conrey et al. 2005), are used to tease apart these possibilities. The Quad model identifies four distinct processes that contribute to responses: (1) the automatic activation of an association; (2) the subject’s ability to determine a correct response (i.e., a response that reflects one’s subjective assessment of truth); (3) the ability to override automatic associations; and (4) general response biases (e.g., favoring right-handed responses). Multinomial modeling has made clear that implicit measures are not “process pure,” i.e., they do not tap into a single unified psychological process.

While there is not consensus about what implicit measures capture (§2), it is clear that they provide at least three kinds of information (Gawronski & Hahn 2019). The first is information about dissociation with more explicit, direct measures. Correlations between implicit and explicit measures tend to be relatively low ( r = .2–.25; Hofmann et al. 2005; Cameron et al. 2012), although these relations are significantly affected by methodological practices, such as comparing non-corresponding implicit and explicit measures (e.g., an implicit measure of gender stereotypes and an explicit “feelings thermometer” toward women). It is important to note the breadth of research in this vein; dissociations between implicit and explicit measures are found in the study of personality (e.g., Vianello et al. 2010), attitudes toward alcohol (e.g., de Houwer et al. 2004), phobias (Teachman & Woody 2003), and more. Second, implicit measures can be used as dependent variables in experiments. Theories about the formation and change of attitudes, for example, have focused on differential effects of manipulations, such as counter-attitudinal information, on implicit and explicit measures (e.g., Gawronski & Bodenhausen 2006; Petty 2006). Third, implicit measures are used to predict behavior. Philosophers have been especially interested in the relationship between implicit bias and discriminatory behavior, particularly when the discriminatory behavior conflicts with a person’s reported beliefs (as in the “Frank” case above). Studies report relationships between implicit bias and behavior in a huge variety of social contexts, from hiring to policing to medicine to teaching and more (for an incomplete list see Table 1 in Jost et al. 2009). There is also voluminous, varied, and on-going discussion about how well implicit measures predict behavior, along with several related critical assessments of the information implicit measures provide (§5).

2. Metaphysics

“Implicit bias” is a term of art, used in a variety of ways. In this entry, the term is used to refer to the family of evaluative judgments and behavior assessed by implicit measures (e.g., categorization judgments on an IAT). These measures mimic some relevant aspects of judgment and decision-making outside the lab (e.g., time pressure). But what do these measures measure? With some blurry boundaries, philosophical and psychological theories can be divided into five groups. Implicit measures might provide information about attitudes (§2.1), implicit processes (§2.2), beliefs (§2.3), traits (§2.4), or situations (§2.5).

The idea that people’s attitudes are the cause of implicit bias is pervasive. The term “attitudes” tends to be used differently in psychology and philosophy, however. In psychology, attitudes are akin to preferences (i.e., likings and dislikings); the term does not refer to propositional states per se (i.e., mental states that are thought to bear a relationship to a proposition), as it does in philosophy. Most attitudinal theories of implicit bias use the term in the psychologist’s sense, although variations will be noted below.

2.1.1 Dual Attitudes in Psychology

Early and influential theories posited that people hold two distinct attitudes in mind toward the same object, one implicit and the other explicit (Greenwald & Banaji 1995; Wilson et al. 2000). “Explicit attitudes” are commonly identified with verbally reported attitudes, in this vein, while “implicit attitudes” are those that a person is unwilling or unable to report. Evidence for theories of dual attitudes stems largely from two sources. The first are anecdotal reports of surprise and consternation that people sometimes express after being informed of their performance on an implicit measure (e.g., Banaji 2011; Krickel 2018). These experiences suggest that people discover their putative implicit attitudes by taking the relevant tests, just like one learns about one’s cholesterol by taking the relevant tests. The second source of evidence for dual-attitude views are dissociations between implicit and explicit measures (§1.2). These suggest that implicit and explicit measures may be tapping into distinct representations of the same attitude-object (e.g., “the elderly”).

A central challenge for theories of this sort is whether people truly are unaware of their implicit biases, and if so, in what way (e.g., if people are unaware of the source, content, or behavioral effects of their attitudes; §3.1). There may be reasons to posit unconscious representations in the human mind independent of whether people are or are not aware of their implicit biases, of course. But if people are aware of their implicit biases, then implicit measures are most likely not assessing unconscious “dual” attitudes.

2.1.2 Dual Attitudes in Philosophy

Some philosophers have proposed that implicit measures assess a distinct kind of “action-oriented” attitude, which is different from ordinary attitudes, but not necessarily in terms of being unconscious. The core idea here is that implicit attitudes link representations with behavioral impulses. [ 6 ] Gendler’s (2008a,b, 2011, 2012) account of “alief,” a sui generis mental state comprised of tightly woven co-activating representational ( R ), affective ( A ), and behavioral ( B ) components, is emblematic of this approach. Gendler argues that the R-A-B components of alief are “bundled” together or “cluster” in such a way that when an implicitly biased person sees a black face in a particular context, for example, the agent’s representation will automatically activate particular feelings and behaviors (i.e., an R–A–B cluster). This is in contrast to the “combinatoric” nature of ordinary beliefs and desires, that is, that any belief could, in principle, be combined with any desire. So while the belief that “that is a black man” is not fixed to any particular feelings or behavior, an alief will have content like, “Black man! Scary! Avoid!”

“To have an alief”, Gendler writes, is

to a reasonable approximation, to have an innate or habitual propensity to respond to an apparent stimulus in a particular way. It is to be in a mental state that is… a ssociative, a utomatic and a rational. As a class, aliefs are states that we share with non-human a nimals; they are developmentally and conceptually a ntecedent to other cognitive attitudes that the creature may go on to develop. Typically, they are also a ffect-laden and a ction-generating. (2008b: 557, original emphasis; see also 2008a: 641)

According to Gendler, aliefs explain a wide array of otherwise puzzling cases of belief-behavior discordance, including not only implicit bias, but also phobias, fictional emotions, and bad habits (2008b: 554). In fact, Gendler suggests (2008a: 663) that aliefs are causally responsible for much of the “moment-by-moment management” of human behavior, whether that behavior is belief-concordant or not.

Critics have raised a number of concerns about this approach, in particular whether putative aliefs form a unified kind (Egan 2011; Currie & Ichino 2012; Doggett 2012; Nagel 2012; Mandelbaum 2013). Others have proposed alternate conceptions of action-oriented dual attitudes. Brownstein and Madva (2012a,b; see also Madva and Brownstein 2018 and Brownstein 2018), for example, propose that implicit attitudes are comprised of F-T-B-A components: the perception of a salient F eature triggers automatic low-level feelings of affective T ension, which are associated in turn with specific B ehavioral responses, which either do or do not A lleviate the agent’s felt tension. This approach shares with Gendler’s the idea that aliefs/implicit attitudes differ in kind from beliefs/explicit attitudes. Moreover, the difference between these putative kinds of states is not necessarily the agent’s introspective access to them. Gendler proposes that while paradigmatic beliefs update when the agent requires new relevant information, paradigmatic aliefs don’t. In contrast, Brownstein and Madva argue that implicit attitudes do update in the face of new information—this is the feed-forward function of “alleviation”—and thus can automatically yet flexibly modify and improve over time. Thus, for Brownstein and Madva, implicit attitudes are implicated not only in bias and prejudice, but also in skillful, intelligent, and even ethical action. [ 7 ] But while implicit attitudes aren’t ballistic, information-insensitive reflexes, on Brownstein and Madva’s view, they also don’t update in the same way as ordinary attitudes. Brownstein and Madva draw the distinction in terms of two key features. First, implicit attitudes are paradigmatically insensitive to the logical form in which information is presented. For example, subjects have been shown to form equivalent implicit attitudes on the basis of information and the negation of that information (e.g., Gawronski et al. 2008). Second, implicit attitudes fail to respond to the semantic contents of other mental states in a systematic way; they appear to be “inferentially impoverished.” For example, implicit attitudes are implicated in behaviors for which it is difficult to give an inferential explanation (e.g., Dovidio et al. 1997) and implicit attitudes change in response to irrelevant information (e.g., Gregg et al. 2006; Han et al. 2006). Levy (2012, 2015)—who argues that implicit attitudes are “patchy endorsements”—makes similar claims about the ways in which implicit attitudes do and do not update, although he does not argue that these kinds of states are “action-oriented” in the way that Gendler and Brownstein and Madva do. Debate about these findings is ongoing (§2.3).

2.1.3 Single Attitudes

Some theories posit the existence of a singular representation of attitude-objects. According to MODE (“Motivation and Opportunity as Determinants”; Fazio 1990; Fazio & Towles-Schwen 1999; Olson & Fazio 2009) and the related MCM (“Meta-Cognitive Model”; Petty 2006; Petty et al. 2007), attitudes are associations between objects and “evaluative knowledge” of those objects. MODE posits one singular representation underlying the behavioral effects measured by implicit and explicit tests. Thus, MODE denies the distinction between implicit and explicit attitudes. The difference between implicit and explicit measures, then, reflects a difference in the control that subjects have over the measured behavior. Control is understood in terms of motivation and opportunity to deliberate. When an agent has low motivation or opportunity to engage in deliberative thought, her automatically activated attitudes—which might be thought of as her “true” attitudes—will guide her behavior and judgment. Implicit measures manufacture this situation (of low control due to low motivation and/or opportunity to deliberate). Explicit measures, by contrast, increase non-attitudinal contributions to test performance. MODE therefore provides empirically-testable predictions about the conditions under which a person’s performance on implicit and explicit measures will converge and diverge, as well as predictions about the conditions under which implicit and explicit measures will and will not predict behavior (see Gawronski & Brannon 2017 for review).

Influenced by dual process theories of mind, RIM (“Reflective-Impulsive Model”; Strack & Deutsche 2004) and APE (“Associative-Propositional Evaluation”; Gawronski & Bodenhausen 2006, 2011) suggest that implicit measures assess distinctive cognitive processes. The central distinction at the heart of both RIM and APE is between “associative” and “propositional” processes. Associative processes are said to underlie an impulsive system that functions according to classic associationist principles of similarity and contiguity. Implicit measures are thought of as assessing the momentary accessibility of elements or nodes of a network of associations. This network produces spontaneous evaluative responses to stimuli. Propositional processes, on the other hand, underlie a reflective system that validates the information provided by activated associations. Explicit measures are thought to capture this process of validation, which is said to operate according to agents’ syllogistic reasoning and judgments of logical consistency. In sum, the key distinction between associative and propositional processes according to RIM and APE is that propositional processing alone depends on an agent’s assessment of the truth of a given representation. [ 8 ] APE in particular aims to explain the interactions between and mutual influences of associative and propositional processes in judgment and behavior.

RIM and APE bear resemblance to the dual attitudes theories in philosophy discussed above. Indeed, Bodenhausen & Gawronski (2014: 957) write that the “distinction between associative and propositional evaluations is analogous to the distinction between ‘alief’ and belief in recent philosophy of epistemology.” It is important to keep in mind, however, that RIM and APE are not attitudinal theories. APE, for example, posits two distinct kinds of process—associative and propositional processes—that give rise to two kinds of evaluative responses to stimuli—implicit and explicit. It does not posit the existence of two distinct attitudes or two distinct co-existing representations of the same entity. It is also important to note that the distinction between associative and propositional processes can be understood in at least three distinct senses: as applying to the way in which information is learned, stored, or expressed (Gawronski et al. 2017). At present, evidence is mixed for dissociation between associative and propositional processing in the learning and storage of information, while it is stronger for dissociation in the behavioral expression of stored information (Brownstein et al. 2019).

2.3 Beliefs

Some have argued that familiar notions of belief, desire, and pretense can in fact explain what neologisms like “implicit attitudes” are meant to elucidate (Egan 2011; Kwong 2012; Mandelbaum 2013). Most defend some version of what Schwitzgebel (2010) calls Contradictory Belief (Egan 2008, 2011; Huebner 2009; Gertler 2011; Huddleston 2012; Muller & Bashour 2011; Mandelbaum 2013, 2014, forthcoming). [ 9 ] Drawing upon theories of the “fragmentation” of the mind (Lewis 1982; Stalnaker 1984), Contradictory Belief holds that implicit and explicit measures both reflect what a person believes, and that these different sets of beliefs may be causally responsible for different behavior in different contexts (Egan 2008). In short, if a person behaves in a manner consistent with the belief that black men are dangerous, it is because they believe that black men are dangerous (notwithstanding what they say they believe).

In the psychological literature, De Houwer and colleagues defend a view that can be thought of as supporting Contradictory Belief (Mitchell et al. 2009; Hughes et al. 2011; De Houwer 2014). On this model, propositions [ 10 ] have three defining features: (1) propositions are statements about the world that specify the nature of the relation between concepts (e.g., “I am good” and “I want to be good” are propositions that involve the same two concepts—“me” and “good”—but differ in the way that the concepts are related); (2) propositions can be formed rapidly on the basis of instructions or inferences; and (3) subjects are conscious of propositions (De Houwer 2014). On the basis of data consistent with these criteria—for example, responses on implicit measures are affected by one-shot instruction—De Houwer (2014) argues that implicit measures capture propositional states (i.e., beliefs). [ 11 ] This claim represents an application of Mitchell and colleagues’ (2009) broader argument that all learning is propositional (i.e., there is no case in which learning is the result of the automatic associative linking of mental representations). One reason philosophers have been interested in this view is due to its resonance with classic debates in the philosophy of mind between empiricists and rationalists, behaviorists and cognitivists, and so on.

Another belief-based approach argues that implicit biases should be understood as cognitive “schemas.” Schemas are clusters of culturally shared concepts and beliefs. More precisely, schemas are abstract knowledge structures that specify the defining features and attributes of a target (Fiske & Linville 1980). The term “mother”, for example, invokes a schema that attributes a collection of attributes to the person so labelled (Haslanger 2015). On some accounts, schemas are “coldly” cognitive (Valian 2005), and so in the psychologist’s sense, they are not attitudes. Rather, schemas are tools for social categorization, and while schemas may help to organize and interpret feelings and motivations, they are themselves affectless. One advantage of focusing on schemas is that doing so emphasizes that implicit bias is not a matter of straightforward antipathy toward members of socially stigmatized groups.

A separate version of the generic belief approach stems from recent work in the philosophy of language. This approach focuses on stereotypes that involve generalizing extreme or horrific behavior from a few individuals to groups. Such generalizations, such as “pit bulls maul children” or “Muslims are terrorists”, can be thought of as a particular kind of generic statement, which Leslie (2017) calls a “striking property generic”. This subclass of generics is defined by having predicates that express properties that people typically have a strong interest in avoiding. Building on earlier work on the cognitive structure and semantics of generics (Leslie 2007, 2008), Leslie notes a particularly insidious feature of social stereotyping: even if just a few members of what is perceived to be an essential kind (e.g., pit bulls, Muslims) exhibit a harmful or dangerous property, then a generic that attributes the property to the kind likely will be judged to be true. This is only the case with striking properties, however. As Leslie (2017) points out, it takes far fewer instances of murder for one to be considered a murderer than it does instances of anxiety to be considered a worrier. Striking property generics may thus illuminate some social stereotypes (e.g., “black men are rapists”) better than others (e.g., “black men are athletic”). Beeghly (2014), however, construes generics as expressions of cognitive schemas, which may broaden the scope of explanation by way of generic statements. In all of these cases, generics involve an array of doxastic properties. Generics involve inferences to dispositions, for example (Leslie 2017). That is, generic statements about striking properties will usually be judged true if and only if some members of the kind possess the property and other members of the kind are judged to be disposed to possess it.

The most explicit defense of Contradictory Belief has been via a theory of “Spinozan Belief Fixation” (SBF; Gilbert 1991; Egan 2008, 2011; Huebner 2009; Mandelbaum 2011, 2013, 2014, 2016). Proponents of SBF are inspired by Spinoza’s rejection of the concept of the will as a cause of free action (Huebner 2009: 68), an idea which is embodied in what they call the theory of “Cartesian Belief Fixation” (CBF). CBF holds that ordinary agents are capable of evaluating the truth of an idea (or representation, or proposition) delivered to the mind (via sensation or imagination) before believing or disbelieving it. Agents can choose to believe or disbelieve P , according to CBF, in other words, via deliberation or judgment. SBF, on the other hand, holds that as soon as an idea is presented to the mind, it is believed. Beliefs on this view are understood to be unconscious propositional attitudes that are formed automatically as soon as an agent registers or tokens their content. For example, one cannot entertain or consider or imagine the proposition that “dogs are made out of paper” without immediately and unavoidably believing that dogs are made out of paper, according to SBF (Mandelbaum 2014). More pointedly, one cannot entertain or imagine the stereotype that “women are bad at math” without believing that women are bad at math. As Mandelbaum (2014) puts it, the automaticity of believing according to SBF explains why people are likely to have many contradictory beliefs; in order to reject P , one must already believe P . [ 12 ]

SBF is strongly revisionist with respect to the ordinary concept of belief (but see Helton (forthcoming) for a similarly spirited but less revisionist view). [ 13 ] Notwithstanding this, the central line of debate about SBF’s account of implicit bias—as well as about belief-based accounts of implicit social cognition generally—focuses on the fact that people’s performance on implicit measures is sometimes unresponsive to the kinds of reinforcement learning based interventions that ought to affect associative processes and/or states; meanwhile, performance on implicit measures sometimes appears to be responsive to the kinds of logical and persuasion based interventions thought to affect doxastic states (e. g., de Houwer 2009, 2014; Hu et al. 2017; Mann & Ferguson 2017; Van Dessel et al. 2018; for additional discussion see Mandelbaum 2013, 2016; Gawronski et al. 2017; Brownstein et al. 2019). Caution is needed in drawing strong conclusions about cognitive structure from these behavioral data, however (Levy 2015; Madva 2016c; Byrd forthcoming; Brownstein et al 2019). As noted above (§1.2), implicit measures are not process-pure. Modeling technique for disentangling the multiple causal contributions to performance on implicit measures may help to move these debates forward (e.g., Conrey et al. 2005; Hütter & Sweldens 2018).

As is the case with terms like “attitude” and “propositional,” psychologists and philosophers tend to use the term “trait” in different ways. In psychology, trait-like constructs are stable over time and across situations. If you have always disliked eating pork, and never eat it no matter the context, then your feelings toward pork are trait-like. If you sometimes decline to eat pork but sometimes indulge, depending on the company or your mood, then your feelings are more “state”-like. In the psychologist’s sense, significant evidence suggests that implicit bias is more state-like than trait-like. Multiple longitudinal studies have found that individuals’ scores on implicit measures vary significantly over days, weeks, and months, much more so than individuals’ scores on corresponding explicit measures (Cooley & Payne 2017; Cunningham et al. 2001; Devine et al. 2012; Gawronski et al. 2017). Of course, the significance of this depends on one’s theory of implicit bias. If implicit measures are theorized to capture spontaneous affective reactions (as APE suggests; §2.2), then contextual and temporal variability in performance should be predicted (because, for example, one’s immediate reactions to images of women leaders will likely be different after watching a documentary about Ruth Bader Ginsburg than after watching Clueless ). However, if implicit measures are meant to “diagnose” stable features of individuals like political party affiliation, then far less variation should be expected. Another possibility is that measurement error contributes significantly to the instability of scores on implicit measures. The fact that methodological improvements have in some cases improved the temporal stability of participants’ performance supports this idea (e.g., Cooley and Payne 2017).

In philosophy, “trait” is used more often in the context of anti-representationalist, dispositional theories of mind. While representationalists define concepts like “belief” in terms of internal, representational structures of the mind, dispositionalists define concepts like “belief” in terms of tendencies to behave in certain ways (and perhaps also to feel and think in certain ways). Building upon Ryle (1949/2009), Schwitzgebel (2006/2010, 2010, 2013) advances a dispositional theory of attitudes (in the philosophical sense, that is, a theory that claims that beliefs, desires, hopes, etc. are dispositions). On his view, attitudes have a broad (or “multitrack”) profile, including dispositions to feel, think, and speak in specific ways. The dispositional profile of a given attitude is determined by the folk-psychological stereotype for having that attitude, not by what’s inside the agent’s metaphoric “belief box.” For example, to establish that Jordan believes that women make good philosophers, one would look to what Jordan says about women philosophers, to her judgments about which philosophers are good and which aren’t, to her hiring practices, her gut feelings around men and women philosophers, etc. Agents with implicit biases pose an interesting challenge to dispositionalists, since these agents often match only part of the relevant folk-psychological stereotypes. For example, Jordan might say that she believes that women make good philosophers but fail to read any women philosophers (or, recall Frank; §1 ). On Schwitzgebel’s “gradualist dispositionalism,” Jordan and Frank would be “in-between believers,” agents who partly match the relevant folk-psychological stereotypes for the attitudes in question.

A related trait-based approach treats the results of indirect measures as reflective of elements of attitudes, rather than as assessing attitudes or biases themselves (Machery 2016, 2017). On Machery’s view, attitudes (in the psychologist’s sense, that is, preferences) are dispositions and are comprised of various bases, including feelings, associations, behavioral impulses, and propositional states like beliefs. (In contrast to Schwitzgebel, Machery holds a representationalist view of belief, but a dispositionalist view of attitudes.) To have a racist attitude, on this picture, is to be disposed to display the relevant mix of these bases, that is, to display the feelings, associations, etc. that together comprise the attitude. Implicit measures, then, are said to capture one of the psychological bases (e.g., her associations between concepts) of the agent’s overall attitude. Explicit questionnaire measures capture another psy­chological basis of the agent’s attitude, behavioral measures yet another basis, and so on. Implicit measures, then, do not assess “implicit attitudes,” and indeed, Machery denies that attitudes divide into implicit and explicit kinds. Rather, implicit measures quantify elements of attitudes. In part, this proposal is meant to explain some of the key psychometric properties of implicit measures, such as their instability over time and the fact that some implicit measures correlate poorly with each other (§5). These findings are consistent with the notion that different implicit measures quantify different psychological bases of attitudes, Machery argues.

One advantage of thinking of implicit biases as traits is that it is consistent with the way in which personality attributions readily admit of vague cases. Just as we might say that Frank is partly agreeable if he extols the virtues of compassion yet sometimes treats strangers rudely, we might say that Frank is partly prejudiced . Dispositional theories capture this intuition. On the other hand, trait-based theories of implicit bias face long-standing challenges to dispositionalism in the philosophy of mind. One such challenge is that traits are explanatory as generalizations, not as token causes of judgment and behavior ( Carruthers 2013). Another is the specter of circularity arising from the simultaneous use of an agent’s behavior to both define her disposition and to point to what her disposition predicts ( Bandura, 1971; Cervone et al. 2015; Mischel 1968; Payne et al. 2017). In both cases, the question for dispositionalism is whether it truly helps to explain the data, or merely repackages outwardly observed patterns in new terms.

The most common way people think and write about implicit biases is as attributes of persons . Another possibility, though, is that implicit biases are attributes of situations . Although psychologists have been debating person-based and situation-based explanations throughout the history of implicit social cognition research (Payne & Gawronski 2010; Murphy & Walton 2013; Murphy et al. 2018), the situationist approach has gained steam due to Payne and colleagues’ (2017) “bias of crowds” model. Borrowing from the concept of the “wisdom of crowds,” this approach suggests that differences between situations explains the variance of scores on implicit measures, rather than differences between individuals. A helpful metaphor used by Payne and colleagues is doing “the wave” at a baseball game. Where a person is sitting in the bleachers, in combination with where the wave is at a given time, is likely to outperform most individual differences (e.g., implicit or explicit feelings about the wave) in predicting whether a person sits or stands. Likewise, what predicts implicit bias are features of people’s situations, not features of their personality. For example, living in a highly residentially segregated neighborhood might be expected to outpredict racial implicit bias compared to individual-level factors, such as beliefs and personality.

The bias of crowds model is aimed at making sense of five features of implicit bias which are otherwise challenging to make sense of together, namely: (1) average group-level scores of implicit bias are very robust and stable; (2) children’s average scores of implicit bias are nearly identical to adults’ average scores; (3) aggregate levels of implicit bias at the population level (e.g., regions, states, and countries) are both highly stable and strongly associated with discriminatory outcomes and group-based disparities; yet, (4) individual differences in implicit bias have small-to-medium zero-order correlations with discriminatory behavior; and (5) individual test-retest reliability is low over weeks and months. (See Payne et al. 2017 for references.) Another advantage of the bias of crowds model is that it coalesces well with calls in philosophy for focusing more on “structural” or “systemic” bias, rather than on the biases in the heads of individuals (§5).

One challenge for the bias of crowds model is explaining how systemic biases interact with and affect the minds of individuals, however. Payne and colleagues appeal to the idea of the “accessibility” of concepts in individuals’ minds, that is, the “likelihood that a thought, evaluation, stereotype, trait, or other piece of information” becomes activated and poised to influence behavior. The lion’s share of evidence, they argue, suggests that the concepts related to implicit bias are activated due to situational causes. This may be, but it does not explain (a) how situations activate concepts in individuals’ minds (Payne and colleagues are explicitly agnostic about the format of cognitive representations that underlie implicit bias); and (b) how situational factors interact with individual factors to give rise to biased actions (Gawronski & Bodenhausen 2017; Brownstein et al. 2019).

3. Epistemology

Philosophical work on the epistemology of implicit bias has focused on three related questions. [ 14 ] First, do we have knowledge of our own implicit biases, and if so, how? Second, do the emerging data on implicit bias demand that we become skeptics about our perceptual beliefs or our overall status as epistemic agents? And third, are we faced with a dilemma between our epistemic and ethical values due to the pervasive nature of implicit bias?

Implicit bias is typically thought of as unconscious (§2.1.1), but what exactly does this mean? There are several possibilities: there might be no phenomenology associated with the relevant mental states or dispositions; agents might be unaware of the content of the representations underlying their performance on implicit measures, or they might be unaware of the source of their implicit biases or the effects those biases have on their behavior; agents might be unaware of the relations between their relevant states (e.g., that their implicit and explicit evaluations of a given target conflict); and agents might have different modes of awareness of their own minds (e.g., “access” vs. “phenomenal” awareness; Block 1995). Gawronski and colleagues (2006) argue that agents typically lack “source” and “impact” awareness of their implicit biases, but typically have “content” awareness. [ 15 ] Evidence for content awareness stems from “bogus pipeline” experiments (e.g., Nier 2005) in which participants are led to believe that inaccurate self-reports will be detected by the experimenter. In these experiments, participants’ scores on implicit and explicit measures come to be more closely correlated, suggesting that participants are aware of the content of those judgments detected by implicit measures and shift their reports when they believe that the experimenter will notice discrepancies. Additional evidence for content awareness is found in studies in which experimenters bring implicit measures and self-reports into conceptual alignment (e.g., Banse et al. 2001) and studies in which agents are asked to predict their own implicit biases (Hahn et al. 2014). Indeed, Hahn and colleagues (2014) and Hahn and Gawronski (2019) have found that people are good at predicting their own IAT scores regardless of how the test is described, how much experience they have taking the test, and how much explanation they are given about the test before taking it. Moreover, people have unique insight into how they will do on the test, insight which is not explained by their beliefs about how people in general will perform.

Hahn and colleagues’ data do not determine, however, whether agents come to be aware of the content of their implicit biases through introspection, by drawing inferences from their own behavior, or from some other source (see Berger forthcoming for discussion). This is important for determining whether the awareness agents have of their implicit biases constitutes self-knowledge. If our awareness of the content of our implicit biases derives from inferences we make based on (for example) our behavior, then the question is whether these inferences are justified, assuming knowledge entails justified true belief. Some have suggested that the facts about implicit bias warrant a “global” skepticism toward our capacities as epistemic agents (Saul 2012; see §3.2.2 ). If this is right, then we ought to worry that our inferences about the content of our implicit biases, from all the ways we behave on a day-to-day basis, are likely to be unjustified. Others, however, have argued that people are typically very good interpreters of their own minds (e.g., Carruthers 2009; Levy 2012), in which case it may be more likely that our inferences about the content of our implicit biases are well-justified. But whether the inferences we make about our own minds are well-justified would be moot if it were shown that we have direct introspective access to our biases.

3.2 Skepticism

One sort of skeptical worry stems from research on the effects of implicit bias on perception ( §3.2.1 ). This leads to a worry about the status of our perceptual beliefs. A second kind of skeptical worry focuses on what implicit bias may tell us about our capacities as epistemic agents in general ( §3.2.2 ).

Compared with participants who were first shown pictures of white faces, those who were primed with black faces in Payne (2001) were faster to identify pictures of guns as guns and were more likely to misidentify pictures of tools as guns. This finding has been directly and conceptually replicated (e.g., Payne et al. 2002; Conrey et al. 2005) and is an instance of a broader set of findings about the effects of attitudes and beliefs on perception (e.g., Barrick et al. 2002; Proffitt 2006). Payne’s findings are chilling particularly in light of police shootings of unarmed black men in recent years, such as Amadou Diallo and Oscar Grant. The findings suggest that agents’ implicit associations between “black men” and “guns” may affect their judgment and behavior by affecting what they see. In addition to the moral implications, this may be cause for a particular kind of epistemic concern. As Siegel (2012, 2017, forthcoming) puts it, the worry is that implicit bias introduces a circular structure into belief formation. If an agent believes that black men are more likely than white men to have or use guns, and this belief causes the agent to more readily see ambiguous objects in the hands of black men as guns, then when the agent relies upon visual perception as evidence to confirm her beliefs, she will have moved in a vicious circle.

Whether implicit biases are cause for this sort of epistemic concern depends on what sort of causal influence social attitudes have on visual perception. Payne’s weapons bias findings would be a case of “cognitive penetration” if the black primes make the images of tools look like images of guns, via an effect on perceptual experience itself (Siegel 2012, 2017, forthcoming). This would certainly introduce a circular structure in belief formation. Other scenarios raise the possibility of illicit belief formation without genuine cognitive penetration. Consider what Siegel calls “perceptual bypass”: the black primes do not cause the tools to look like guns (i.e., the prime does not cause a change in perceptual experience), yet some state in the agent, such as a heightened state of anxiety, is affected by the black prime and causes the agent to make a classification error. This will count as a case of illicit belief formation inasmuch as the agent’s social attitudes cause her to be insensitive to her visual stimuli in a way that confirms her antecedent attitudes (Siegel 2012). Other scenarios might allay the worry about illicit belief formation. For example, what Siegel calls “disowned behavior” proposes the same route to the classification error as “perceptual bypass,” except that the agent antecedently regards her error as an error. Empirical evidence can help to sort through these possibilities, though perhaps not settle between them conclusively (e.g., Correll et al. 2015).

A broader worry is that research on implicit bias should cause agents to mistrust their knowledge-seeking faculties in general. “Bias-related doubt” (Saul 2012) is stronger than traditional forms of skepticism (e.g., external world skepticism) in the sense that it suggests that our epistemic judgments are not just possibly but often likely mistaken. Implicit biases are likely to degrade our judgments across many domains, e.g., professors’ judgments about student grades, journal submissions, and job candidates. [ 16 ] Moreover, as Fricker (2007) points out, the testimony of members of stigmatized groups is likely to be discounted due to implicit bias, which, Saul suggests, can magnify these epistemic failures as well as create others, such as failing to recognize certain questions as relevant for inquiry (Hookway 2010). The key point about these examples is that our judgments are likely to be affected by implicit biases even when “we think we’re making judgments of scientific or argumentative merit” (Saul 2012: 249; see also Welpinghus forthcoming). Moreover, unlike errors of probabilistic reasoning, these effects generalize across many areas of day-to-day life. We should be worried, Saul argues,

whenever we consider a claim, an argument, a suggestion, a question, etc from a person whose apparent social group we’re in a position to recognize. (Saul 2012: 250).

Bias-related doubt may be diminished if successful interventions can be developed to correct for epistemic errors caused by implicit bias. In some cases, the fix may be simple, such as anonymous review of job candidate dossiers. But other contexts will certainly be more challenging. [ 17 ] More generally, Saul’s account of bias-related doubt takes a strongly pessimistic stance toward the normativity of our unreflective habits. “It is difficult to see”, she writes, “how we could ever properly trust [our habits] again once we have reflected on implicit bias” (2012: 254). Others, however, have stressed the ways in which unreflective habits can have epistemic virtues (e.g., Arpaly 2004; Railton 2014; Brownstein & Madva 2012a,b; Nagel 2012; Antony 2016). Squaring the reasons for pessimism about the epistemic status of our habits with these streams of thought will be important in future research.

Gendler (2011) and Egan (2011) argue that implicit bias creates a conflict between our ethical and epistemic aims. Concern about ethical/epistemic dilemmas is at least as old as Pascal, as Egan points out, but is also incarnated in contemporary research on the value of positive illusions (i.e., beliefs like “I am brilliant!” which may promote well-being despite being false; e.g., Taylor & Brown 1988). The dilemma surrounding implicit bias stems from the apparent unavoidability of stereotyping, which Gendler traces to the way in which social categorization is fundamental to our cognitive capacities. [ 18 ] For agents who disavow common social stereotypes for ethical reasons, this creates a conflict between what we know and what we value. As Gendler puts it,

if you live in a society structured by racial categories that you disavow, either you must pay the epistemic cost of failing to encode certain sorts of base-rate or background information about cultural categories, or you must expend epistemic energy regulating the inevitable associations to which that information—encoded in ways to guarantee availability—gives rise. (2011: 37)

Gender considers forbidden base rates, for example, which are useful statistical generalizations that utilize problematic social knowledge. People who are asked to set insurance premiums for hypothetical neighborhoods will accept actuarial risk as a justification for setting higher premiums for particular neighborhoods but will not do so if they are told that actuarial risk is correlated with the racial composition of that neighborhood (Tetlock et al. 2000). This “epistemic self-censorship on non-epistemic grounds” makes it putatively impossible for agents to be both rational and equitable (Gendler 2011: 55, 57).

Egan (2011) raises problems for intuitive ways of diffusing this dilemma, settling instead on the idea that making epistemic sacrifices for our ethical values may simply be worth it. Others have been more unwilling to accept that implicit bias does in fact create an unavoidable ethical-epistemic dilemma (Mugg 2013; Beeghly 2014; Madva 2016b; Lassiter & Ballantyne 2017; Puddifoot 2017). One way of diffusing the dilemma, for example, is to suggest that it is not social knowledge per se that has costs, but rather that the accessibility of social knowledge in the wrong circumstances has cognitive costs (Madva 2016b). The solution to the dilemma, then, is not ignorance, but the situation-specific regulation of stereotype accessibility. For example, the accessibility of social knowledge can be regulated by agents’ goals and habits (Moskowitz & Li 2011). Readers interested in ethical-epistemic dilemmas due to implicit bias should also consider related scholarship on “moral encroachment” (e.g., Basu & Schroeder 2018; Gardiner 2018).

Most philosophical writing on the ethics of implicit bias has focused on two distinct (but related) questions. First, are agents morally responsible for their implicit biases ( §4.1 )? Second, can agents change their implicit biases or control their effects on their judgments and behavior ( §4.2 )?

4.1 Moral Responsibility

Researchers working on moral responsibility for implicit bias often make two key distinctions. First, they distinguish responsibility for attitudes from responsibility for judgments and behavior. One can, that is, ask whether agents are responsible for their putative (§2) implicit attitudes as such, or whether agents are responsible for the effects of their implicit attitudes on their judgments and behavior. Most have focused on the latter question, as will I. A second important distinction is between being responsible and holding responsible. This distinction can be glossed in a number of different but related ways. It can be glossed as a distinction between blameworthiness and actual expressions of blame; between backward- and forward-looking responsibility (i.e., responsibility for things one has done in the past versus responsibility for doing certain things in the future); and between responsibility as a form of judgment versus responsibility as a form of sanction. Most have focused on the former of these disjuncts (being responsible, blameworthiness, etc.) via three kinds of approaches: arguments from the importance of awareness or knowledge of one’s implicit biases ( §4.1.1 ); arguments from the importance of control over the impact of one’s implicit biases on one’s judgment and behavior ( §4.1.2 ); and arguments from “attributionist” and “Deep Self” considerations ( §4.1.3 ; see Holroyd et al. 2017 for a more in-depth review of theories of moral responsibility and implicit bias).

It is plausible that conscious awareness of our implicit biases is a necessary condition for moral responsibility for those biases. Saul articulates the intuitive idea, suggesting that we

abandon the view that all biases against stigmatised groups are blameworthy … [because a] person should not be blamed for an implicit bias that they are completely unaware of, which results solely from the fact that they live in a sexist culture. (2013: 55, emphasis in original)

Saul’s claim appears to be in keeping with folk psychological attitudes about blameworthiness and implicit bias. Cameron and colleagues (2010) found that subjects were considerably more willing to ascribe moral responsibility to “John” when he was described as acting in discriminatory ways against black people despite “thinking that people should be treated equally, regardless of race” compared to when he was described as acting in discriminatory ways despite having a “sub-conscious dislike for African Americans” that he is “unaware of having”.

Recalling the evidence that people often do have awareness of their implicit biases ( §3.1 ), it would seem that typical agents are responsible for those biases on the basis of the argument from awareness. However, if the question is whether agents are blameworthy for behaviors affected by implicit biases (rather than for having biases themselves), then perhaps impact awareness is what matters most (Holroyd 2012). That said, lacking impact awareness of the effects of implicit bias on our behavior may not exculpate agents from responsibility even in principle. One possibility is that implicit biases are analogous to moods in the sense that being in an introspectively unnoticed bad mood can cause one to act badly (Madva 2018). There is debate about whether unnoticed moods are exculpatory (e.g., Korsgaard 1997; Levy 2011). One possibility is that bad moods and implicit biases both diminish blameworthiness, but do not undermine it as such. This claim depends in part on moral responsibility admitting of degrees.

One problem with focusing on impact awareness, however, as Holroyd (2012) points out, is that we may be unaware of the impact of a great many cognitive states on our behavior. The focus on impact awareness may lead to a global skepticism about moral responsibility, in other words. This suggests that impact awareness may not serve as a good criterion for distinguishing responsibility for implicit biases from responsibility for other cognitive states, notwithstanding whether global skepticism about moral responsibility is defensible.

A second way to unpack the argument from awareness is to focus on what agents ought to know about implicit bias, rather than what they do know. This approach indexes moral responsibility to one’s social and epistemic environment. For example, Kelly & Roedder (2008) argue that a “savvy grader” is responsible for adjusting her grades to compensate for her likely biases because she ought to be aware of and compelled by research on implicit bias. In a similar spirit, Washington & Kelly (2016) compare two hypothetical egalitarians with equivalent psychological profiles, the only difference between them being that the “Old School Egalitarian” is evaluating résumés in 1980 and the “New Egalitarian” is doing so in 2014. While neither has heard of implicit bias, Washington & Kelly argue that the New Egalitarian is morally culpable in a way that the Old School Egalitarian isn’t. Only the New Egalitarian could have, and ought to have, known about his likely implicit biases, given the comparative states of art of psychological research in 1980 and 2014. The underlying intuition here is that assessments of responsibility change with changes in an agent’s social and epistemic environment.

A third way of unpacking the argument from awareness is to focus on the way in which an attitude does or does not integrate with a variety of the agent’s other attitudes once it becomes conscious (Levy 2012; see §2.1 ). On this view, attitudes that cause responsible behavior are available to a broad range of cognitive systems. For example, in cognitive dissonance experiments (e.g., Festinger 1956), agents attribute confabulatory reasons to themselves and then tend to act in accord with those self-attributed reasons. The self-attribution of reasons in this case, according to Levy (2012), has an integrating effect on behavior, and thus can be thought of as underwriting the sort of agency required for moral responsibility. Crucially, it is when the agent becomes conscious of her self-attributed reasons that they have this integrating effect. This provides grounds for claiming that attitudes for which agents are responsible are those that integrate behavior when the agent becomes aware of the content of those attitudes. Implicit attitudes are not like this, according to Levy. What’s morally important is that

awareness of the content of our implicit attitudes fails to integrate them into our person level concerns in the manner required for direct moral responsibility. (Levy 2012: 9).

The fact that implicit processes are often defined in contrast to “controlled” cognitive processes (§2.2) implies that they may affect behavior in a way that bypasses a person’s agential capacities. The fact that implicit biases seem to “rebound” in response to intentional efforts to suppress them supports this interpretation (Huebner 2009; Follenfant & Ric 2010). Early research suggesting that implicit biases reflect mere awareness of stereotypes, rather than personal attitudes, also implies that these states reflect processes that “happen to” agents. More recently, however, philosophers have questioned the ramifications of these and other data for the notion of control relevant to moral responsibility.

Perhaps the most familiar way of understanding control in the responsibility literature is in terms of a psychological mechanism that would allow an agent to act differently than she otherwise would act when there is sufficient reason to do so (Fischer & Ravizza 2000). The question facing this sort of reasons-responsiveness view of control is whether automatized behaviors—which unfold in the absence of explicit reasoning—should be thought of as under an agent’s control. Some have argued that automaticity and control are not mutually exclusive. Holroyd & Kelly (2016) advance a notion of “ecological control”, and Suhler and Churchland (2009) offer an account of nonconscious control that underwrites automaticity itself, yet is ostensibly sufficient for underwriting responsibility. Others have distinguished between automaticity and automatisms (e.g., sleepwalking); in this sense, the relevant moral distinction might be drawn in terms of agents’ ability to “pre-program” their automatic actions (but not automatistic actions) via previous controlled choices (e.g., Wigley 2007); it might be drawn in terms of agents’ ability to consciously monitor their automatic actions (e.g., Levy & Bayne, 2004); or it might simply be the case that putative implicit attitudes are not automatic because they are readily changeable (e.g., Buckwalter forthcoming). [ 19 ] Others still have distinguished between “indirect” and “direct” control over one’s attitudes or behavior (e.g., Holroyd 2012; Levy & Mandelbaum 2014; Sie & Voorst Vader-Bours 2016). Holroyd (2012) argues that there are many things over which we do not hold direct and immediate control, yet for which we are commonly held responsible, such as learning a skill, speaking a foreign language, and even holding certain beliefs. None of these abilities or states can be had by fiat of will; rather, they take time and effort to obtain. This suggests that we can be held responsible for attitudes or behaviors over which we only have indirect long-range control. The question, then, of course, is whether agents can exercise indirect long-range control over their implicit biases. Mounting evidence suggests that we can ( §4.2 ).

“Attributionist” and Deep Self theories of moral responsibility represent an alternative to arguments from awareness and control. According to these theories, for an agent to be responsible for an action is for that action to “reflect upon” the agent “herself”. A common way of speaking is to say that responsibility-bearing actions are attributable to agents in virtue of reflecting upon the agent’s “deep self”, where the deep self represents the person’s fundamental evaluative stance (Sripada 2016). Although there is much disagreement in the literature about what the deep self really is, as well as what it means for an attitude or action to reflect upon it, attributionists agree that people can be morally responsible for actions that are non-conscious (e.g., “failure to notice” cases), non-voluntary (e.g., actions stemming from strong emotional reactions), or otherwise divergent from an agent’s will (Frankfurt 1971; Watson 1975, 1996; Scanlon 1998; A. Smith 2005, 2008, 2012; Hieronymi 2008; Sher 2009; and H. Smith 2011).

One influential view developed in recent years is that agents are responsible for just those actions or attitudes that stem from, or are susceptible to modification by, the agent’s “evaluative” or “rational” judgments, which are judgments for which it is appropriate (in principle) to ask the agent her reasons (in a justifying sense) for holding (Scanlon 1998; A. Smith 2005, 2008, 2012). A. Smith suggests that implicit biases stem from rational judgments, because

a person’s explicitly avowed beliefs do not settle the question of what she regards as a justifying consideration. (2012: 581–582, fn 10)

An alternative approach sees the source of the “deep self” in an agent’s “cares” rather than in her rational judgments (Shoemaker 2003, 2011; Jaworska 2007; Sripada 2016). Cares have been described in different ways, but in this context are thought of as psychological states with motivational, affective, and evaluative dispositional properties. It is an open question whether implicit biases are reflective of an agent’s cares (Brownstein 2016a, 2018). It is also possible that even in cases in which an implicit bias is not attributable to an agent’s deep self, it may still be appropriate to hold the agent responsible for violating some duty or obligation she holds due to her implicit biases (Zheng 2016). Glasgow (2016) similarly argues for responsibility for implicit biases that may not be attributable to agents. His view unfolds in terms of responsibility for actions from which agents are nevertheless alienated. Glasgow defends this view on the basis of “Content-Sensitive Variantism” and “Harm-Sensitive Variantism”, a pair of views according to which alienation exculpates depending on extra-agential features of an action, such as the content of the action or the kind of harm it creates. These variantist views are fairly strongly revisionist with respect to traditional conceptions of responsibility in the 20 th century philosophical literature. Some have argued that research on implicit bias calls for revisionism of this sort (Vargas 2005; Faucher 2016).

4.2 Interventions

Researchers working in applied ethics may be less concerned with questions about in-principle culpability and more concerned with investigating how to change or control our implicit biases. Of course, anyone committed to fighting against prejudice and discrimination will likely share this interest. Policymakers and workplace managers may also be concerned with finding effective interventions, given that they are already directing tremendous public and private resources toward anti-discrimination programs in workplaces, universities, and other domains affected by intergroup conflict. Yet as Paluck and Green (2009) suggest, the effectiveness of many of the strategies commonly used remains unclear. Most studies on prejudice reduction are non-experimental (lacking random assignment), are performed without control groups, focus on self-report surveys, and gather primarily qualitative (rather than quantitative) data.

An emerging body of laboratory-based research suggests that strategies are available for regulating implicit biases, however. One way to class these strategies is in terms of those that purport to change the apparent associations underlying agents’ implicit biases, compared with those that purport to leave implicit associations intact but enable agents to control the effects of their biases on their judgment and behavior (Stewart & Payne 2008; Mendoza et al. 2010; Lai et al. 2013). For example, a “change-based” strategy might reduce individuals’ automatic associations of “white” with “good” while a “control-based” strategy might enable individuals to prevent that association from affecting their behavior. Below, I briefly describe some of these interventions. For comparison of the data on their effectiveness, see Lai and colleagues (2014, 2016), and for discussion of their significance for theories of the metaphysics of implicit bias, including a helpful appendix listing “debiasing” experiments, see Byrd (forthcoming).

Intergroup contact (Aberson et al. 2008; Dasgupta & Rivera 2008; Anderson 2010 for discussion): long studied for its effects on explicit prejudice (e.g., Allport 1954; Pettigrew & Tropp 2006), interaction between members of different social groups appears to diminish implicit bias as well, albeit under some moderating conditions (e.g., equal status interaction) and not under others.

Approach training (Kawakami et al. 2007, 2008; Phills et al. 2011): participants repeatedly “negate” stereotypes and “affirm” counter-stereotypes by pressing a button labelled “NO!” when they see stereotype-consistent images (e.g., of a black face paired with the word “athletic”) or “YES!” when they see stereotype-inconsistent images (e.g., of a white face paired with the word “athletic”). Other experimental scenarios have had participants push a joystick away from themselves to “negate” stereotypes and pull the joystick toward themselves to “affirm” counter-stereotypes.

Evaluative conditioning (Olson & Fazio 2006; De Houwer 2011): a widely used technique whereby an attitude object (e.g., a picture of a black face) is paired with another valenced attitude object (e.g., the word “genius”), which shifts the valence of the first object in the direction of the second.

Counter-stereotype exposure (Blair et al. 2001; Dasgupta & Greenwald 2001): increasing individuals’ exposure to images, film clips, or even mental imagery depicting members of stigmatized groups acting in stereotype-discordant ways (e.g., images of female scientists).

Implementation intentions (Gollwitzer & Sheeran 2006; Stewart & Payne 2008; Mendoza et al. 2010; Webb et al. 2012): “if-then” plans that specify a goal-directed response that an individual plans to perform on encountering an anticipated cue. For example, in a “Shooter Bias” test, where participants are given the goal to “shoot” all and only those individuals shown holding guns in a computer simulation, participants may be asked to adopt the plan, “if I see a black face, I will think ‘safe!’” [ 20 ]

“Cues for control” (Monteith 1993; Monteith et al. 2002): techniques for noticing prejudiced responses, in particular the affective discomfort caused by the inconsistency of those responses with participants’ egalitarian goals.

Priming goals, moods, and motivations (Huntsinger et al. 2010; Moskowitz & Li 2011; Mann & Kawakami 2012): priming egalitarian goals, multicultural ideologies, or particular moods can lower scores of prejudice on implicit measures.

There is some doubt about this way of categorizing interventions, as some control-based interventions may also change agents’ underlying associations and some association-based interventions may also promote control (Stewart & Payne 2008; Mendoza et al. 2010). More significant though are concerns about the efficacy of these interventions over time (Lai et al. 2016), their practical feasibility (Bargh 1999; Schneider 2004), and the possibility that they may distract from broader problems of economic and institutional forms of injustice (Anderson 2010; Dixon et al. 2012; see §5 ). Of course, most of the research on interventions like these is recent, so it is simply not clear yet which strategies, or combination of strategies (Devine et al. 2012), will or won’t be effective. Some have voiced optimism about the role lab-based interventions like these can play as elements of broader efforts to combat prejudice and discrimination (e.g., Kelly et al. 2010a; Madva 2017).

5. Critical Responses

Research on implicit bias has been criticized in several ways. Below are brief descriptions of, and discussion about, prominent lines of critique. [ 21 ] I leave aside critical assessments of specific implicit measures.

Research on implicit bias has received a lot of attention, not only in philosophy and psychology, but in politics, journalism, jurisprudence, business, and medicine as well. Some have worried that this attention is excessive, such that the explanatory power of research on implicit bias has been overstated (e.g., Singal 2017; Jussim 2018 (Other Internet Resources); Blanton & Ikizer 2019).

While the difficulty of public science communication is pervasive (i.e., not limited to implicit bias research), and the most egregious cases are found in the popular press, it is true that some researchers have overhyped the importance of implicit bias for explaining social phenomena. Hype can have disastrous consequences, such as creating public distrust in science. One important point to bear in mind, however, is that the challenges facing science communication and the challenges facing a body of research are distinct. That is, one question is whether the science is strong, and it is a separate question whether the strength of the science, such as it is, is accurately communicated to the public. Overhyped research may create incentives for scientists to do flashy but weak work—and this is a problem—but problems with hype are nevertheless distinct from problems with the science itself.

Some have argued that explicit bias can explain much of what implicit bias purports to explain (e.g., Hermanson 2017a,b, 2018 (Other Internet Resources); Singal 2017; Buckwalter 2018). Jesse Singal (2017), for example, denies that implicit bias is more important than explicit bias, pointing to the United States Department of Justice’s findings about intentional race-based discrimination in Ferguson, MO and to the fact that the United States elected a relatively explicitly racist President in 2016.

Singal and others are surely right that explicit bias and outright prejudice are persistent and, in some places, pervasive. It is, however, unclear who, if anyone, thinks that implicit bias is more important than explicit bias. Philosophers in particular have been interested in implicit bias because, despite the persistence and pervasiveness of explicit bias, there are many people—presumably many of those reading this article—who aim to think and act in unprejudiced ways, and yet are susceptible to the kinds of biased behavior implicit bias researchers have studied. This is not only an important phenomenon in its own right, but also may contribute causally to the mainstream complacence toward the very outrageous instances of bigotry Singal discusses. Implicit bias may also contribute causally to explicit bias, particularly in environments suffused with prejudiced norms (Madva 2019).

A related worry is that there is not agreement in the literature about what “implicit” means. Arguably the most common understanding is that “implicit” means “unconscious.” But whatever is assessed by implicit measures is arguably not unconscious (§3.1).

It is true that there is no widespread agreement about the meaning of “implicit,” and it is also true that no theory of implicit social cognition is consistent with all the current data. To what extent this is a problem depends on background theories about how science progresses. It is also crucial to recognize that implicit measures are not high-fidelity assessments of any one distinct “part” of the mind. They are not process pure (§1.2). This means that they capture a mix of various cognitive and affective processes. Included in this mix are people’s beliefs and explicit attitudes. Indeed, researchers have known for some time that the best way to predict a person’s scores on an implicit measure like the IAT is to ask them their opinions about the IAT’s targets. This does not mean that implicit measures lack “discriminant validity,” however (i.e., that they are redundant with existing measures). By analogy, you are likely to find that people who say that cilantro is disgusting are likely to have aversive reactions to it, but this doesn’t mean that their aversive reactions are an invalid construct. Indeed, one of the leading theories of the dynamics and processes of implicit social cognition since 2006—APE (§2.2)—is based on a set of predictions about this process impurity (i.e., about the interactions of implicit and explicit evaluative processes).

Several meta-analyses have found that, according to standard conventions, the correlation between implicit measures and behavior is small to medium. Average correlations have ranged from approximately .14 to .37 ( Cameron et al. 2012; Greenwald et al. 2009; Oswald et al. 2013; Kurdi et al. 2019 ). This variety is due to several factors, including the type of measures, type of attitudes measured (e.g., attitudes in general vs. intergroup attitudes in particular), inclusion criteria for meta-analyses, and statistical meta-analytic techniques. From these data, critics have concluded that implicit measures are poor predictors of behavior. Oswald and colleagues write, “the IAT provides little insight into who will discriminate against whom, and provides no more insight than explicit measures of bias” (2013, 18). Focusing on implicit bias research more broadly, Buckwalter suggests that a review of the evidence “casts doubt on the claim that implicit attitudes will be found to be significant causes of behavior” (2018, 11).

Several background questions must be considered in order to assess these claims. Should implicit measures be expected to have small, medium, or large unconditional (or “zero-order”) correlations with behavior? Zero-order correlations are those that obtain between two variables when no additional variable has been controlled for. Since the 1970s, research on self-reported attitudes has largely focused on when —under what conditions—attitudes predict behavior, not whether attitudes predict behavior just as such. For example, attitudes better predict behavior when there is clear correspondence between the attitude object and the behavior in question ( Ajzen & Fishbein 1977). While generic attitudes toward the environment do not predict recycling behavior very well, for instance, specific attitudes toward recycling do ( Oskamp et al. 1991). In the 1970s and 1980s, a consensus emerged that attitude-behavior relations depend in general on the particular behavior being measured (e.g., political judgments vs. racial judgments), the conditions under which the behavior is performed (e.g., under time pressure or not), and the person who is performing the behavior (e.g., personality; Zanna & Fazio 1982). A wealth of theoretical models of attitude-behavior relations take these facts into account to make principled predictions about when attitudes do and do not predict behavior (e.g., Fazio 1990 ). Similar work is underway focusing on implicit social cognition (for review see Gawronski & Hahn 2019 and Brownstein et al. ms).

In a related vein, it is also important to keep in mind that large zero-order correlations are rarely found in social science, let alone in attitude research. Large zero-order correlations should not be expected to be found in implicit bias research, either ( Gawronski, forthcoming ). Indeed, the zero-order correlations between other familiar constructs and outcome measures is comparable to what has been found in meta-analyses of implicit measures: beliefs and stereotypes about outgroups and behavior ( r = .12; Talaska et al. 2008); IQ and income ( r = .2–.3; Strenze 2007); SAT scores and freshman grades in college ( r = .24; Wolfe and Johnson 1995); parents’ and their children’s socioeconomic status ( r = .2–.3; Strenze 2007). The fact that no meta-analysis of implicit measures has reported nonsignificant correlations close to zero or negative correlations with behavior further supports the conclusion that the relationship between implicit bias and behavior falls within the “zone” of the relationship between these more familiar constructs and relevant kinds of behavior. Whether this common pattern of findings in social science—of weak to moderate unconditional relations with behavior—is succor for supporters of implicit bias research or cause for concern about the social sciences in general is an important and open question (see, e.g., Greenwald et al. 2015; Oswald et al. 2015; Jost 2019; Gawronski forthcoming). [ 22 ] But note that the consistent finding of meta-analyses of implicit measures distinguishes this body of research from those that have been swept up in the social sciences’ ongoing “replication crisis.” That people, on average, display biases on implicit measures is one of the most stable and replicated findings in recent psychological science. [ 23 ] The debate described in this section pertains to interpreting the significance of this finding.

So-called “structuralist” critics (e.g., Banks & Ford 2009; Anderson 2010; Haslanger 2015; Ayala 2016, 2018; Mallon ms) have argued that researchers ought to pay more attention to systemic and institutional causes of injustice—such as poverty, housing segregation, economic inequality, etc.—rather than focusing on the biases inside the minds of individuals. One way to express the structuralist idea is that what happens in the minds of individuals, including their biases, is the product of social inequities rather than an explanation for them. Structuralists then tend to argue that our efforts to combat discrimination and inequity ought to focus on changing social structures themselves, rather than trying to change individual’s biases directly. For example, Ayala argues that “agents’ mental states [are] … not necessary to understand and explain” when considering social injustice (2016, 9). Likewise, in her call to combat segregation in the contemporary United States, Anderson (2010) is critical of what she sees as a distracting focus on the psychology of bias.

A strong version of the structuralist critique—that research on the psychology of prejudice is entirely useless, distracting, or even dangerous—is hard to defend. Large-scale demographic research makes clear that psychological prejudice is a key driver of (for example) economic inequality (e.g., Chetty et al. 2018 ) and inequities in the criminal justice system ( Center for Policing Equity 2016 ). More broadly, no matter how autonomously certain social structures operate, people must choose to accept or reject those structures, to vote for politicians who speak for or against them, and so on. How people assess these options is at least in part a psychological question.

A weaker version of the structuralist critique calls for needed attention to the ways in which psychological and structural phenomena interact to produce and entrench discrimination and inequity. This “interactionism” seeks to understand how bias operates differently in different contexts. If you wanted to combat housing segregation, for example, you would want to consider not only problematic institutional practices, such as “redlining” certain neighborhoods within which banks will not give mortgage loans, and not only psychological factors, such as the propensity to perceive low-income people as untrustworthy, but the interaction of the two. A low-income person from a redlined neighborhood might not be perceived as untrustworthy when they are interviewing for a job as a nanny, but might be perceived as untrustworthy when they are interviewing for a loan. Adopting the view that bias and structure interact to produce unequal outcomes does not mean that researchers must always account for both. Sometimes it makes sense to emphasize one kind of cause or the other.

An interactionist version of structuralism can incorporate research on prejudice into a wider understanding of inequity, rather than eschew it. One way to do so is to identify ways in which psychological biases (whether implicit or explicit) might be key contributors to social-structural phenomena. For example, structuralists sometimes point to the drug laws and sentencing guidelines that contribute to the mass incarceration of black men in the USA as examples of systemic biases. Sometimes, however, when these laws and policies change, discrimination persists. While arrests have declined for all racial groups in states that have decriminalized marijuana, black people continue to be arrested for marijuana-related offenses at a rate of about 10 times that of white people ( Drug Policy Alliance 2018 ). This suggests that psychological biases (belonging to officers, policy makers, or voters) are an ineliminable part of systemic inequity. Such interactionism is just one approach for blending individual and institutional approaches to intergroup discrimination (see, e.g., Madva 2016a, 2017; Davidson & Kelly forthcoming). Another idea is to incorporate research specifically on implicit bias into a wider understanding of the structural sources of inequity by using implicit measures to assess broad social patterns (rather than to assess the differences between individuals). The “Bias of Crowds” model (§2.5) argues that implicit bias is a feature of cultures and communities. For example, average scores on implicit measures of prejudice and stereotypes, when aggregated at the level of cities within the United States, predict racial disparities of shootings of citizens by police in those cities ( Hehman et al. 2017). Thus, while it is certainly true that most of the relevant literature and discussion conceptualizes implicit bias as way of differentiating between individuals, structuralists might utilize the data for differentiating regions, cultures, and so on.

Nosek and colleagues (2011) suggest that the second generation of research on implicit social cognition will come to be known as the “Age of Mechanism”. Several metaphysical questions fall under this label. One question crucial to the metaphysics of implicit bias is whether the relevant psychological constructs should be thought of as stable, trait-like features of a person’s identity or as momentary, state-like features of their current mindset or situation (§2.4). While current data suggest that implicit biases are more state-like than trait-like, methodological improvements may generate more stable, dispositional results on implicit measures. Ongoing research on additional psychometric properties of implicit measures—such as their discriminant validity and capacity to predict behavior—will also strengthen support for some theories of the metaphysics of implicit bias and weaken support for others. Another open metaphysical question is whether the mechanisms underlying different forms of implicit bias (e.g., implicit racial biases vs. implicit gender biases) are heterogeneous. Some have already begun to carve implicit social attitudes into kinds (Amodio & Devine 2006; Holroyd & Sweetman 2016; Del Pinal et al. 2017; Del Pinal & Spaulding 2018; Madva & Brownstein 2018). Future research on implicit bias in particular domains of social life may also help to illuminate this issue, such as research on implicit bias in legal practices (e.g., Lane et al. 2007; Kang 2009) and in medicine (e.g., Green et al. 2007; Penner et al. 2010), on the development of implicit bias in children (e.g., Dunham et al. 2013b), on implicit intergroup bias toward non-black racial minorities, such as Asians and Latinos (Dasgupta 2004), and cross-cultural research on implicit bias in non-Western countries (e.g., Dunham et al. 2013a).

Future research on epistemology and implicit bias may tackle a number of questions, for example: does the testimony of social and personality psychologists about statistical regularities justify believing that you are biased ? What can developments in vision science tell us about illicit belief formation due to implicit bias? In what ways is implicit bias depicted and discussed outside academia (e.g., in stand-up comedy focusing on social attitudes)? Also germane are future methodological questions, such as how research on implicit social cognition may interface with large-scale correlational sociological studies on social attitudes and discrimination (Lee 2016). Another crucial methodological question is whether and how theories of implicit bias—and more generally psychological approaches to understanding social phenomena—can come to be integrated with broader social theories focusing on race, gender, class, disability, etc. Important discussions have begun (e.g., Valian 2005; Kelly & Roedder 2008; Faucher & Machery 2009; Anderson 2010; Machery et al. 2010; Madva 2017), but there is no doubt that more connections must be drawn to relevant work on identity (e.g., Appiah 2005), critical theory (e.g., Delgado & Stefancic 2012), feminist epistemology (Grasswick 2013), and race and political theory (e.g., Mills 1999).

As with all of the above, questions in theoretical ethics about moral responsibility for implicit bias will certainly be influenced by future empirical research. One noteworthy intersection of theoretical ethics with forthcoming empirical research will focus on the interpersonal effects of blaming and judgments about blameworthiness for implicit bias. [ 24 ] This research aims to have practical ramifications for mitigating intergroup conflict as well, of course. On this front, arguably the most pressing question, however, is about the durability of psychological interventions once agents leave the lab. How long will shifts in biased responding last? Will individuals inevitably “relearn” their biases (cf. Madva 2017)? Is it possible to leverage the lessons of “situationism” in reverse, such that shifts in individuals’ attitudes create environments that provoke more egalitarian behaviors in others (Sarkissian 2010; Brownstein 2016b)? Moreover, what has (or has not) changed in people’s feelings, judgments, and actions now that research on implicit bias has received considerable public attention (e.g., Charlesworth & Banaji 2019)?

• Aberson, C., M. Porter, & A. Gaffney, 2008, “Friendships predict Hispanic student’s implicit attitudes toward Whites relative to African Americans”, Hispanic Journal of Behavioral Sciences , 30: 544–556.
• Ajzen, I., & M. Fishbein, 1977, “Attitude-behavior relations: A theoretical analysis and review of empirical research,” Psychological Bulletin, 84: 888–918.
• Allport, G., 1954, The Nature of Prejudice , Reading: Addison-Wesley.
• Amodio, D. & P. Devine, 2006, “Stereotyping and evaluation in implicit race bias: evidence for independent constructs and unique effects on behavior”, Journal of Personality and Social Psychology , 91(4): 652.
• –––, 2009, “On the interpersonal functions of implicit stereotyping and evaluative race bias: Insights from social neuroscience”, in Attitudes: Insights from the new wave of implicit measures , R. Petty, R. Fazio, & P. Briñol (eds.), Hillsdale, NJ: Erlbaum, pp. 193–226.
• Amodio, D. & K. Ratner, 2011, “A memory systems model of implicit social cognition”, Current Directions in Psychological Science , 20(3): 143–148.
• Anderson, E., 2010, The Imperative of Integration , Princeton: Princeton University Press.
• –––, 2012, “Epistemic justice as a virtue of social institutions”, Social Epistemology , 26(2): 163–173.
• Antony, L., 2016, “Bias: friend or foe? Reflections on Saulish Skepticism”, in Brownstein & Saul (eds.) 2016A.
• Appiah, A., 2005, The Ethics of Identity , Princeton: Princeton University Press.
• Arkes, H. & P. Tetlock, 2004, “Attributions of implicit prejudice, or ‘would Jesse Jackson ‘fail’ the Implicit Association Test?’”, Psychological Inquiry , 15: 257–278.
• Aronson, E. & C. Cope, 1968, “My Enemy’s Enemy is My Friend”, Journal of Personality and Social Psychology , 8(1): 8–12.
• Arpaly, N., 2004, Unprincipled Virtue: An Inquiry into Moral Agency , Oxford: Oxford University Press.
• Ashburn-Nardo, L., M. Knowles, & M. Monteith, 2003, “Black Americans’ implicit racial associations and their implications for intergroup judgment”, Social Cognition , 21:1, 61–87.
• Ayala, S., 2016, “Speech affordances: A structural take on how much we can do with our words,” European Journal of Philosophy , 24: 879–891.
• –––, 2018, “A Structural Explanation of Injustice in Conversations: It’s about Norms,” Pacific Philosophical Quarterly , 99(4): 726–748. doi:10.1111/papq.12244
• Banaji, M. & A. Greenwald, 2013, Blindspot , New York: Delacorte Press.
• Banaji, M. & C. Hardin, 1996, “Automatic stereotyping”, Psychological Science , 7(3): 136–141.
• Banaji, M., C. Hardin, & A. Rothman, 1993, “Implicit stereotyping in person judgment”, Journal of personality and Social Psychology , 65(2): 272.
• Bandura, A., 1978, “The self system in reciprocal determinism,” American Psychologist , 33: 344–358. Banks, R. R., & R.T. Ford, 2008, “(How) Does Unconscious Bias Matter: law, Politics, and Racial Inequality,” Emory LJ , 58: 1053–1152.
• Banse, R., J. Seise, & N. Zerbes, 2001, “Implicit attitudes towards homosexuality: Reliability, validity, and controllability of the IAT”, Zeitschrift für Experimentelle Psychologie , 48: 145–160.
• Bar-Anan, Y. & B. Nosek, 2014, “A Comparative Investigation of Seven Implicit Measures of Social Cognition”, Behavior Research Methods , 46(3): 668–688. doi:10.3758/s13428-013-0410-6
• Bargh, J., 1994, “The four horsemen of automaticity: Awareness, intention, efficiency, and control in social cognition”, in Handbook of social cognition (2nd ed.) , R. Wyer, Jr. & T. Srull (eds.), Hillsdale, NJ: Lawrence Erlbaum Associates, Inc., pp 1–40.
• –––, 1999, “The cognitive monster: The case against the controllability of automatic stereotype effects”, in Chaiken & Trope (eds.) 1999: 361–382.
• Barrick, C., D. Taylor, & E. Correa, 2002, “Color sensitivity and mood disorders: biology or metaphor?”, Journal of affective disorders , 68(1): 67–71.
• Basu, R. & M. Schroeder, 2018, “Doxastic Wronging,” in B. Kim & M. McGrath (eds), Pragmatic Encroachment in Epistemology , New York: Routledge.
• Beeghly, E., 2014, Seeing Difference: The Epistemology and Ethics of Stereotyping , PhD diss., University of California, Berkeley, California.
• Beeghly, E. & A. Madva, forthcoming, An Introduction to Implicit Bias: Knowledge, Justice, and the Social Mind , New York: Routledge.
• Begby, E., 2013, “The Epistemology of Prejudice”, Thought: A Journal of Philosophy , 2(2): 90–99.
• Berger, J., forthcoming, “Implicit attitudes and awareness,” Synthese , first online 14 March 2018. doi:10.1007/s11229-018-1754-3
• Bertrand, M., D. Chugh, & S. Mullainathan, 2005, “Implicit discrimination”, American Economic Review , 94–98.
• Bertrand, M. & S. Mullainathan, 2004, “Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market”, NBER Working Papers from National Bureau of Economic Research, Inc., No. 9873.
• Blanton, H., & E.G. Ikizer, 2019, “Elegant Science Narratives and Unintended Influences: An Agenda for the Science of Science Communication,” Social Issues and Policy Review , 13(1): 154–181.
• Blair, I., J. Ma, & A. Lenton, 2001, “Imagining Stereotypes Away: The Moderation of Implicit Stereotypes through Mental Imagery”, Journal of personality and social psychology , 81(5): 828–841.
• Block, N., 1995, “On a confusion about a function of consciousness,” Behavioral and Brain Sciences , 18: 227–287.
• Bodenhausen, G. & B. Gawronski, 2014, “Attitude Change”, in The Oxford Handbook of Cognitive Psychology , D. Reisberg (ed.), New York: Oxford University Press.
• Brennan, S., 2013, “Rethinking the Moral Significance of Micro-Inequities: The Case of Women in Philosophy”, in Women in Philosophy: What Needs to Change?, F. Jenkins and K. Hutchinson (eds.), Oxford: Oxford University Press.
• Brewer, M., 1999, “The psychology of prejudice: Ingroup love and outgroup hate?”, Journal of social issues , 55(3): 429–444.
• Brownstein, M., 2016a, “Attributionism and moral responsibility for implicit bias, Review of Philosophy and Psychology , 7(4): 765–786.
• –––, 2016b, “Implicit Bias, Context, and Character”, in Brownstein & Saul (eds.) 2016B.
• –––, 2018, The Implicit Mind: Cognitive Architecture, the Self, and Ethics , New York: Oxford University Press.
• –––, forthcoming, “Skepticism about Bias,” in Beeghly & Madva (eds.), forthcoming.
• Brownstein, M. & A. Madva, 2012a, “Ethical Automaticity”, Philosophy of the Social Sciences , 42(1): 67–97.
• –––, 2012b, “The Normativity of Automaticity”, Mind and Language , 27(4): 410–434.
• Brownstein, M., Madva, A., & Gawronski, B., 2019, “What do implicit measures measure?,” WIREs Cognitive Science . doi:10.1002/wcs.1501
• Brownstein, M. & J. Saul (eds.), 2016A, Implicit Bias & Philosophy: Volume I, Metaphysics and Epistemology , Oxford: Oxford University Press.
• ––– (eds.), 2016B, Implicit Bias and Philosophy: Volume 2, Moral Responsibility, Structural Injustice, and Ethics , Oxford: Oxford University Press.
• Buckwalter, W., forthcoming, “Implicit attitudes and the ability argument,” Philosophical Studies , first online 15 September 2018. doi:10.1007/s11098-018-1159-7 [ available online ]
• Byrd, N., forthcoming, “What we can (and can’t) infer about implicit bias from debiasing experiments,” Synthese , first online 12 February 2019. doi:10.1007/s11229-019-02128-6
• Cameron, C., B. Payne, & J. Knobe, 2010, “Do theories of implicit race bias change moral judgments?”, Social Justice Research , 23: 272–289.
• Cameron, C.D., Brown-Iannuzzi, J. L., & B.K. Payne, B. K., 2012, “Sequential priming measures of implicit social cognition: A meta-analysis of associations with behavior and explicit attitudes,” Personality and Social Psychology Review , 16: 330–350.
• Carruthers, P., 2009, “How we know our own minds: the relationship between mindreading and metacognition”, Behavioral and Brain Sciences , 32: 121–138.
• –––, 2013, “On knowing your own beliefs: A representationalist account,” in N. Nottelmann (ed.), New essays on belief: Constitution, content and structure , pp. 145– 165, Basingstoke: Palgrave MacMillan.Center for Policing Equity, 2016, [ available online ].
• Cervone, D. Caldwell, T. L., & N.D. Mayer, 2015, “Personality systems and the coherence of social behavior,” In. B. Gawronski & G. V. Bodenhausen (Eds.), Theory and explanation in social psychology , pp. 157–179, New York: Guilford.
• Chaiken, S. & Y. Trope (eds.), 1999, Dual-process theories in social psychology , New York: Guilford Press.
• Chetty, R., Hendren, N., Jones, M. R., & S.R. Porter, 2018, “Race and economic opportunity in the United States: An intergenerational perspective (No. w24441),” National Bureau of Economic Research , [ available online ].
• Clark, A., 1997, Being There: Putting Brain, Body, and World Together Again , Cambridge, MA: MIT Press.Cooley, E., & B. K. Payne, 2017, “Using groups to measure intergroup prejudice. Personality and Social Psychology Bulletin ,” 43: 46–59.
• Conrey, F., J. Sherman, B. Gawronski, K. Hugenberg, & C. Groom, 2005, “Separating multiple processes in implicit social cognition: The Quad-Model of implicit task performance”, Journal of Personality and Social Psychology , 89: 469–487.
• Correll, J., B. Park, C. Judd, & B. Wittenbrink, 2002, “The police officer’s dilemma: Using race to disambiguate potentially threatening individuals”, Journal of Personality and Social Psychology , 83: 1314–1329.
• Correll, J., Wittenbrink, B., Crawford, M., & M. Sadler, 2015, “Stereotypic Vision: How Stereotypes Disambiguate Visual Stimuli,” Journal of Personality and Social Psychology , 108(2): 219–233.
• Cortina, L., 2008, “Unseen injustice: Incivility as modern discrimination in organizations”, Academy of Management Review , 33: 55–75.
• Cortina, L., D. Kabat Farr, E. Leskinen, M. Huerta, & V. Magley, 2011, “Selective incivility as modern discrimination in organizations: Evidence and impact”, Journal of Management , 39(6): 1579–1605.
• Cunningham, W. & P. Zelazo, 2007, “Attitudes and evaluations: A social cognitive neuroscience perspective”, Trends in cognitive sciences , 11(3): 97–104.
• Cunningham, W., P. Zelazo, D. Packer, & J. Van Bavel, 2007, “The iterative reprocessing model: A multilevel framework for attitudes and evaluation”, Social Cognition , 25(5): 736–760.
• Currie, G. & A. Ichino, 2012, “Aliefs don’t exist, but some of their relatives do”, Analysis , 72: 788–798.
• Dasgupta, N., 2004, “Implicit Ingroup Favoritism, Outgroup Favoritism, and Their Behavioral Manifestations”, Social Justice Research , 17(2): 143–168.
• Dasgupta, N. & A. Greenwald, 2001, “On the malleability of automatic attitudes: Combating automatic prejudice with images of admired and disliked individuals”, Journal of Personality and Social Psychology , 81: 800–814.
• Dasgupta, N. & L. Rivera, 2008, “When social context matters: The influence of long-term contact and short-term exposure to admired group members on implicit attitudes and behavioral intentions”, Social Cognition , 26: 112–123.
• Davidson, L. J., & D. Kelly, forthcoming, “Minding the gap: Bias, soft structures, and the double life of social norms,” Journal of Applied Philosophy , first online 23 December 2018. doi:10.1111/japp.12351
• De Houwer, J., 2009, “The propositional approach to associative learning as an alternative for association formation models,” Learning & Behavior , 37: 1–20.
• –––, 2011, “Evaluative Conditioning: A review of functional knowledge and mental process theories”, in Associative Learning and Conditioning Theory: Human and Non-Human Applications , T. Schachtman and S. Reilly (eds.), Oxford: Oxford University Press, pp. 399–417.
• –––, 2014, “A Propositional Model of Implicit Evaluation”, Social Psychology and Personality Compass , 8(7): 342–353.
• De Houwer, J., G. Crombez, E. Koster, & N. Beul, 2004, “Implicit alcohol-related cognitions in a clinical sample of heavy drinkers”, Journal of behavior therapy and experimental psychiatry , 35(4): 275–286.
• De Houwer, J., S. Teige-Mocigemba, A. Spruyt, & A. Moors, 2009, “Implicit measures: A normative analysis and review”, Psychological bulletin , 135(3): 347.
• Del Pinal, G., A. Madva & K. Reuter, 2017, “Stereotypes, conceptual centrality and gender bias: An empirical investigation,” Ratio , 30(4): 384–410.
• Del Pinal, G., & S. Spaulding, 2018, “Conceptual centrality and implicit bias,” Mind & Language , 33(1): 95–111.
• Delgado, R. & J. Stefancic, 2012, Critical race theory: An introduction , New York: NYU Press.
• Devine, P., 1989, “Stereotypes and prejudice: Their automatic and controlled components”, Journal of Personality and Social Psychology , 56: 5–18.
• Devine, P., P. Forscher, A. Austin, & W. Cox, 2012, “Long-term reduction in implicit race bias; a prejudice habit-breaking intervention”, Journal of Experimental Social Psychology , 48(6): 1267–1278.
• Devine, P. & M. Monteith, 1999, “Automaticity and control in stereotyping”, in Chaiken & Trope (eds.) 1999: 339–360.
• Dixon, J., M. Levine, S. Reicher, & K. Durrheim, 2012, “Beyond prejudice: Are negative evaluations the problem and is getting us to like one another more the solution?”, Behavioral and Brain Sciences , 35(6): 411–425.
• Doggett, T., 2012, “Some questions for Tamar Szabó Gendler”, Analysis , 72: 764–774.
• Dovidio, J. & S. Gaertner, 1986, Prejudice, Discrimination, and Racism: Historical Trends and Contemporary Approaches , Academic Press.
• –––, 2004, “Aversive racism”, Advances in experimental social psychology , 36: 1–51.
• Dovidio, J., K. Kawakami, & S. Gaertner, 2002, “Implicit and explicit prejudice and interracial interaction”, Journal of Personality and Social Psychology ,82: 62–68.
• Dovidio, J., K. Kawakami, C. Johnson, B. Johnson, & A. Howard, 1997, “On the nature of prejudice: Automatic and controlled processes”, Journal of Experimental Social Psychology , 33: 510–540.
• Dreyfus, H. & S. Dreyfus, 1992, “What is Moral Maturity? Towards a Phenomenology of Ethical Expertise”, in Revisioning Philosophy , J. Ogilvy (ed.), Albany: State University of New York.Drug Policy Alliance, 2018, “From Prohibition to Progress: A Status Report on Marijuana Legalization,” [ available online ].
• Dunham, Y., M. Srinivasan, R. Dotsch, & D. Barner, 2013a, “Religion insulates ingroup evaluations: the development of intergroup attitudes in India”, Developmental Science , 17(2): 311–319. doi:10.1111/desc.12105
• Dunham, Y., E. Chen, & M. Banaji, 2013b, “Two Signatures of Implicit Intergroup Attitudes Developmental Invariance and Early Enculturation”, Psychological science , 24(6) 860–868.
• Egan, A., 2008, “Seeing and believing: perception, belief formation and the divided mind”, Philosophical Studies , 140(1): 47–63.
• –––, 2011. “Comments on Gendler’s ‘The epistemic costs of implicit bias,’”, Philosophical Studies , 156: 65–79.
• Faucher, L., 2016, “Revisionism and Moral Responsibility”, in Brownstein & Saul (eds.) 2016B.
• Faucher, L. & E. Machery, 2009, “Racism: Against Jorge Garcia’s moral and psychological monism”, Philosophy of the Social Sciences , 39: 41–62.
• Fazio, R., 1990, “Multiple processes by which attitudes guide behavior: The MODE model as an integrative framework”, Advances in experimental social psychology , 23: 75–109.
• –––, 1995, “Attitudes as object-evaluation associations: Determinants, consequences, and correlates of attitude accessibility”, in Attitude strength: Antecedents and consequences ( Ohio State University series on attitudes and persuasion, Vol. 4 ), R. Petty & J. Krosnick (eds.), Hillsdale, NJ: Lawrence Erlbaum Associates, Inc., pp. 247–282.
• Fazio, R. & T. Towles-Schwen, 1999, “The MODE model of attitude-behavior processes”, in Chaiken & Trope (eds.) 1999: 97–116.
• Festinger, L., 1956, A theory of cognitive dissonance , Stanford, CA: Stanford University Press.
• Fischer, J. & M. Ravizza, 2000, Responsibility and control: A theory of moral responsibility , Cambridge: Cambridge University Press.
• Fiske, S. & P. Linville, 1980, “What does the schema concept buy us?”, Personality and Social Psychology Bulletin , 6(4): 543–557.
• Follenfant, A. & F. Ric, 2010, “Behavioral Rebound following stereotype suppression”, European Journal of Social Psychology , 40: 774–782.
• Frankfurt, H., 1971, “Freedom of the Will and the Concept of a Person”, The Journal of Philosophy , 68(1): 5–20.
• Frankish, K., 2016, “Implicit bias, dual process, and metacognitive motivation”, in Brownstein & Saul (eds.) 2016A.
• Fricker, M., 2007, Epistemic Injustice: Power & the Ethics of Knowing , Oxford: Oxford University Press.
• Friese, M., W. Hofmann, & M. Wänke, 2008, “When impulses take over: Moderated predictive validity of explicit and implicit attitude measures in predicting food choice and consumption behavior”, British Journal of Social Psychology , 47(3): 397–419.
• Galdi, S., L. Arcuri, & B. Gawronski, 2008, “Automatic mental associations predict future choices of undecided decision-makers”, Science , 321(5892): 1100–1102.
• Gardiner, G., 2018, “Evidentialism and Moral Encroachment,” in K. McCain (ed.), Believing in Accordance with the Evidence , Cham, Switzerland: Springer.
• Gawronski, B., forthcoming, “Six lessons for a cogent science of implicit bias and its criticism,” Perspectives on Psychological Science .
• Gawronski, B. & G. Bodenhausen, 2006, “Associative and propositional processes in evaluation: an integrative review of implicit and explicit attitude change”, Psychological bulletin , 132(5): 692–731.
• –––, 2011, “The associative-propositional evaluation model: Theory, evidence, and open questions”, Advances in Experimental Social Psychology , 44: 59–127.
• –––, 2017, “Beyond persons and situations: An interactionist approach to implicit bias,” Psychological Inquiry , 28(4): 268–272.
• Gawronski, B. & S. Brannon, 2017, “Attitudes and the Implicit-Explicit Dualism,” in The Handbook of Attitudes, Volume 1: Basic Principles (2 nd Edition) , D. Albarracín & B.T. Johnson (eds.), New York: Routledge, pp. 158–196.
• Gawronski, B., R. Deutsch, S. Mbirkou, B. Seibt, & F. Strack, 2008, “When ‘Just Say No’ is not enough: Affirmation versus negation training and the reduction of automatic stereotype activation”, Journal of Experimental Social Psychology , 44: 370–377.
• Gawronski, B. & A. Hahn, 2019, “Implicit Measures: Procedures, Use, and Interpretation,” in Measurement in Social Psychology , Blanton, H., LaCroix, J.M., and G.D. Webster (eds.), New York: Taylor & Francis, 29–55.
• Gawronski, B., W. Hofmann, & C. Wilbur, 2006, “Are “implicit attitudes unconscious?”, Consciousness and Cognition , 15: 485–499.Gawronski, B., Morrison, M., Phills, C., & Galdi, S.,2017, “Temporal Stability of Implicit and Explicit Measures: A Longitudinal Analysis,” Personality and Social Psychology Bulletin, 43: 300–312.
• Gawronski, B., E. Walther, & H. Blank, 2005, “Cognitive Consistency and the Formation of Interpersonal Attitudes: Cognitive Balance Affects the Encoding of Social Information”, Journal of Experimental Social Psychology , 41: 618–26.
• Gendler, T., 2008a, “Alief and belief”, The Journal of Philosophy , 105(10): 634–663.
• –––, 2008b, “Alief in action (and reaction)”, Mind and Language , 23(5): 552–585.
• –––, 2011, “On the epistemic costs of implicit bias”, Philosophical Studies , 156: 33–63.
• –––, 2012, “Between reason and reflex: response to commentators”, Analysis , 72(4): 799–811.
• Gertler, B., 2011, “Self-Knowledge and the Transparency of Belief”, in Self-Knowledge , A. Hatzimoysis (ed.), Oxford: Oxford University Press.
• Gilbert, D., 1991, “How mental systems believe”, American Psychologist , 46: 107–119.
• Glaser, J. & E. Knowles, 2008, “Implicit motivation to control prejudice”, Journal of Experimental Social Psychology , 44: 164–172.
• Glasgow, J., 2016, “Alienation and Responsibility”, in Brownstein & Saul (eds.) 2016B.
• Gollwitzer, P. & P. Sheeran, 2006, “Implementation intentions and goal achievement: A meta-analysis of effects and processes”, in Advances in experimental social psychology , M. Zanna (ed.), Academic Press, pp. 69–119.
• Grasswick, H., 2013, “Feminist Social Epistemology”, The Stanford Encyclopedia of Philosophy , (Spring 2013 edition), E. Zalta (ed.), < https://plato.stanford.edu/archives/spr2013/entries/feminist-social-epistemology/ >.
• Green, A., D. Carney, D. Pallin, L. Ngo, K. Raymond, L. Lezzoni, & M. Banaji, 2007, “Implicit bias among physicians and its prediction of thrombolysis decisions for black and white patients”, Journal of General Internal Medicine , 22: 1231–1238.
• Greenwald, A. & M. Banaji, 1995, “Implicit social cognition: attitudes, self-esteem, and stereotypes”, Psychological review , 102(1): 4.
• Greenwald, A., M. Banaji, & B. Nosek, 2015, “Statistically Small Effects of the Implicit Association Test Can Have Societally Large Effects”, Journal of personality and social psychology , 108(4): 553–561.
• Greenwald, A. & S. Farnham, 2000, “Using the implicit association test to measure self-esteem and self-concept”, Journal of personality and social psychology , 79(6): 1022–1038.
• Greenwald, A., D. McGhee, & J. Schwartz, 1998, “Measuring individual differences in implicit cognition: The implicit association test”, Journal of Personality and Social Psychology , 74: 1464–1480.
• Greenwald, A., B. Nosek, & M. Banaji, 2003, “Understanding and using the implicit association test: I. An improved scoring algorithm”, Journal of personality and social psychology , 85(2): 197–216.
• Greenwald, A., T. Poehlman, E. Uhlmann, & M. Banaji, 2009, “Understanding and Using the Implicit Association Test: III Meta-Analysis of Predictive Validity”, Journal of Personality and Social Psychology , 97(1): 17–41.
• Gregg A., B. Seibt, & M. Banaji, 2006, “Easier done than undone: Asymmetry in the malleability of implicit preferences”, Journal of Personality and Social Psychology , 90: 1–20.
• Hahn, A. & B. Gawronski, 2019, “Facing One’s Implicit Biases: From Awareness to Acknowledgement,” Journal of Personality and Social Psychology: Interpersonal Relations and Group Processes , 116(5): 769–794.
• Hahn, A., C. Judd, H. Hirsh, & I. Blair, 2014, “Awareness of Implicit Attitudes”, Journal of Experimental Psychology-General , 143(3): 1369–1392.
• Han, H., M. Olson, & R. Fazio, 2006, “The influence of experimentally-created extrapersonal associations on the Implicit Association Test”, Journal of Experimental Social Psychology , 42: 259–272.
• Harari, H. & J. McDavid, 1973, “Name stereotypes and teachers’ expectations”, Journal of Educational Psychology , 65(2): 222–225.
• Haslanger, S., 2000, “Gender and race:(what) are they? (What) do we want them to be?”, Nous , 34(1): 31–55.
• –––, 2015, “Social Structure, Narrative, and Explanation,” Canadian Journal of Philosophy , 45(1): 1–15.
• Hehman, E., Flake, J. K., & J. Calanchini, J., 2017, “Disproportionate use of lethal force in policing is associated with regional racial biases of residents,” Social Psychological and Personality Science , 1948550617711229.
• Heider, F., 1958, The Psychology of Interpersonal Relations , New York: Wiley.
• Hermanson, S., 2017a, “Implicit Bias, Stereotype Threat, and Political Correctness in Philosophy,” Philosophies , 2(2): 12. doi:10.3390/philosophies2020012 [ available online ].
• –––, 2017b, “Review of Implicit Bias and Philosophy (vol. 1 & 2), Edited by Michael Brownstein and Jennifer Saul, Oxford University Press, 2016,” The Journal of the Royal Institute of Philosophy , 315—322.
• Helton, G., forthcoming, “If you can’t change what you believe, you don’t believe it,” Nous , first online 26 August 2018. doi:10.1111/nous.12265
• Hieronymi, P., 2008, “Responsibility for believing”, Synthese , 161: 357–373.
• Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & M. Schmitt, 2005, “A meta-analysis on the correlation between the Implicit Association Test and explicit self-report measures,” Personality and Social Psychology Bulletin , 31(10): 1369–1385.
• Holroyd, J., 2012, “Responsibility for Implicit Bias”, Journal of Social Philosophy , 43(3): 274–306.
• Holroyd, J. and D. Kelly, 2016, “Implicit Bias, Character, and Control.” in J. Webber and A. Masala (eds.) From Personality to Virtue , Oxford: Oxford University Press.
• Holroyd, J., Scaife, R., & T. Stafford, T., 2017, “Responsibility for implicit bias,” Philosophy Compass , 12(3). doi:10.1111/phc3.12410
• Holroyd, J. & J. Sweetman, 2016, “The Heterogeneity of Implicit Biases”, in Brownstein & Saul (eds.) 2016A.
• Hookway, C., 2010, “Some Varieties of Epistemic Injustice: Response to Fricker”, Episteme , 7(2): 151–163.
• Houben, K. & R. Wiers, 2008, “Implicitly positive about alcohol? Implicit positive associations predict drinking behavior”, Addictive behaviors , 33(8): 979–986.
• Hu, X., Gawronski, B., & R. Balas, 2017, “Propositional versus dual-process accounts of evaluative conditioning: I. The effects of co-occurrence and relational information on implicit and explicit evaluations,” Personality and Social Psychology Bulletin , 43: 17–32.
• Huddleston, A., 2012, “Naughty beliefs”, Philosophical studies , 160(2): 209–222.
• Huebner, B., 2009, “Trouble with Stereotypes for Spinozan Minds”, Philosophy of the Social Sciences , 39: 63–92.
• –––, 2016, “Implicit Bias, Reinforcement Learning, and Scaffolded Moral Cognition”, in Brownstein & Saul (eds.) 2016A.
• Hughes, S., D. Barnes-Holmes, & J. De Houwer, 2011, “The dominance of associative theorizing in implicit attitude research: Propositional and behavioral alternatives”, The Psychological Record , 61(3): 465–498.
• Hundleby, C., 2016, “The Status Quo Fallacy: Implicit Bias and Fallacies of Argumentation”, in Brownstein & Saul (eds.) 2016A.
• Huntsinger, J., S. Sinclair, E. Dunn, & G. Clore, 2010, “Affective regulation of stereotype activation: It’s the (accessible) thought that counts”, Personality and Social Psychology Bulletin , 36(4): 564–577.
• Hütter, M., & S. Sweldens, S., 2018, “Dissociating controllable and uncontrollable effects of affective stimuli on attitudes and consumption,” Journal of Consumer Research , 45: 320–349.
• Jacoby, L. & M. Dallas, 1981, “On the relationship between autobiographical memory and perceptual learning”, Journal of Experimental Psychology: General , 110(3): 306.
• James, W., 1890/1950, The Principles of Psychology, Volumes 1&2 , New York: Dover Books.
• Jaworska, A., 2007, “Caring and Internality”, Philosophy and Phenomenological Research , 74(3): 529–568.
• Jost, J. T., 2019, “The IAT is dead, long live the IAT: Context-sensitive measures of implicit attitudes are indispensable to social and political psychology,” Current Directions in Psychological Science , 28(1): 10–19.
• Jost, J. T., Rudman, L. A., Blair, I. V., Carney, D. R., Dasgupta, N., Glaser, J., & C.D. Hardin, 2009, “The existence of implicit bias is beyond reasonable doubt: A refutation of ideological and methodological objections and executive summary of ten studies that no manager should ignore,” Research in organizational behavior , 29: 39–69.
• Kang, J., 2009, “Implicit Bias: A Primer for Courts”, National Center for State Courts.
• Kang, J., M. Bennett, D. Carbado, P. Casey, N. Dasgupta, D. Faigman, R. Godsil, A. Greenwald, J. Levinson, & J. Mnookin, 2012, “Implicit bias in the courtroom”, UCLA Law Review , 59(5): 1124–1186.
• Kawakami, K., J. Dovidio, & S. van Kamp, 2007, “The Impact of Counterstereotypic Training and Related Correction Processes on the Application of Stereotypes”, Group Processes and Intergroup Relations , 10(2): 139–156.
• Kawakami, K., J. Steele, C. Cifa, C. Phills, & J. Dovidio, 2008, “Approaching math increases math = me, math = pleasant”, Journal of Experimental Social Psychology , 44: 818–825.
• Kelly, D., L. Faucher, & E. Machery, 2010a, “Getting Rid of Racism: Assessing Three Proposals in Light of Psychological Evidence”, Journal of Social Philosophy , 41(3): 293–322.
• Kelly, D., E. Machery, & R. Mallon, 2010b, “Race and Racial Cognition”, in The Moral Psychology Handbook , J. Doris & the Moral Psychology Reading Group (eds.), Oxford: Oxford University Press, pp. 433–472.
• Kelly, D. & E. Roedder, 2008, “Racial Cognition and the Ethics of Implicit Bias”, Philosophy Compass , 3(3): 522–540.
• Korsgaard, C., 1997, “The Normativity of Instrumental Reason”, in Ethics and Practical Reason , G. Cullity & B. Gaut (eds.), Oxford: Clarendon Press, pp 27–68.
• Krickel, B., 2018, “Are the states underlying implicit biases unconscious? A Neo-Freudian answer,” Philosophical Psychology , 31(7): 1007–1026.
• Kurdi, B., Seitchik, A., Axt, J., Carroll, T., Karapetyan, A., Kaushik, N., Tomezsko, D., Greenwald, A., and M. Banaji, M., 2019, “Relationship between the Implicit Association Test and intergroup behavior: A meta-analysis, American Psychologist 74(5): 569–586. doi:10.1037/amp0000364
• Kwong, J., 2012, “Resisting Aliefs: Gendler on Alief-Discordant Behaviors”, Philosophical Psychology , 25(1): 77–91.
• Lai, C., K. Hoffman, & B. Nosek, 2013, “Reducing implicit prejudice”, Social and Personality Psychology Compass , 7: 315–330.
• Lai, C., M. Marini, S. Lehr, C. Cerruti, J. Shin, J. Joy-Gaba, A. Ho, … & B. Nosek, 2014, “Reducing implicit racial preferences: I. A comparative investigation of 17 interventions”, Journal of Experimental Psychology: General , 143(4): 1765–1785.
• Lai, C. K., Skinner, A. L., Cooley, E., Murrar, S., Brauer, M., Devos, T., … & S. Simon, 2016, “Reducing implicit racial preferences: II. Intervention effectiveness across time,” Journal of Experimental Psychology: General , 145(8): 1001–1016.
• Lane, K., J. Kang, & M. Banaji, 2007, “Implicit Social Cognition and Law”, Annual Review of Law and Social Science , 3: 427–451.
• Lassiter, C., & N. Ballantyne, N., 2017, “Implicit racial bias and epistemic pessimism,” Philosophical Psychology , 30(1-2): 79–101.
• Lee, C., 2016, “Revisiting Current Causes of Women’s Underrepresentation in Science”, in Brownstein & Saul (eds.) 2016A.
• Leslie, S., 2007, “Generics and the Structure of the Mind”, Philosophical Perspectives , 375–405.
• –––, 2008, “Generics: Cognition and Acquisition”, Philosophical Review , 117(1): 1–49.
• –––, 2017, “The original sin of cognition: Fear, prejudice, and generalization”, The Journal of Philosophy , 114(8): 393–421.
• Levinson, J., 2007, “Forgotten Racial Equality: Implicit Bias, Decision making, and Misremembering”, Duke Law Journal , 57(2): 345–424.
• Levy, N., 2011, “Expressing Who We Are: Moral Responsibility and Awareness of our Reasons for Action”, Analytic Philosophy , 52(4): 243–261.
• –––, 2012, “Consciousness, Implicit Attitudes, and Moral Responsibility”, Noûs , 48: 21–40.
• –––, 2015, “Neither fish nor fowl: Implicit attitudes as patchy endorsements”, Noûs , 49(4): 800–823.
• Levy, N. & T. Bayne, 2004, “Doing without deliberation: automatism, automaticity, and moral accountability”, International Review of Psychiatry , 16(3): 209–215.
• Levy, N. & E. Mandelbaum, 2014, “The Powers that Bind: Doxastic Voluntarism and Epistemic Obligation”, in The Ethics of Belief , J. Matheson & R. Vitz (eds.), Oxford: Oxford University Press.
• Lewis, D., 1982, “Logic for Equivocators”, Nous , 431–441.
• Machery, E., 2016, “De-Freuding Implicit Attitudes”, in Brownstein & Saul (eds.) 2016A.
• –––, 2017, “Do Indirect Measures of Biases Measure Traits or Situations?,” Psychological Inquiry , 28(4): 288–291.
• Machery, E. & L. Faucher, 2005, “Social construction and the concept of race”, Philosophy of Science , 72(5): 1208–1219.
• Machery, E., Faucher, L. & D. Kelly, 2010, “On the alleged inadequacy of psychological explanations of racism”, The Monist , 93(2): 228–255.
• Madva, A., 2012, The hidden mechanisms of prejudice: Implicit bias and interpersonal fluency , PhD dissertation, Columbia University.
• –––, 2016a, “A plea for anti-anti-individualism: How oversimple psychology misleads social policy,” Ergo, an Open Access Journal of Philosophy , 3.
• –––, 2016b, “Virtue, Social Knowledge, and Implicit Bias”, in Brownstein & Saul (eds.) 2016A.
• –––, 2016c, “Why implicit attitudes are (probably) not beliefs,” Synthese , 193(8): 2659–2684.
• –––, 2017, “Biased Against De-Biasing: On the Role of (Institutionally Sponsored) Self-Transformation in the Struggle Against Prejudice,” Ergo , 4: 145–179.
• –––, 2018, “Implicit bias, moods, and moral responsibility,” Pacific Philosophical Quarterly , 99: 53–78.
• –––, 2019, “Social Psychology, Phenomenology, and the Indeterminate Content of Unreflective Racial Bias,” in E. S. Lee (Ed.), Race as Phenomena . New York: Rowman and Littlefield.
• Madva, A. & M. Brownstein, 2018, “Stereotypes, Prejudice, and the Taxonomy of the Implicit Social Mind,” Nous , 53(2): 611–644.
• Mai, R., S. Hoffmann, J. Helmert, B. Velichkovsky, S. Zahn, D. Jaros, … & H. Rohm, 2011, “Implicit food associations as obstacles to healthy nutrition: the need for further research”, The British Journal of Diabetes & Vascular Disease , 11(4): 182–186.
• Maison, D., A. Greenwald, & R. Bruin, 2004, “Predictive validity of the Implicit Association Test in studies of brands, consumer attitudes, and behavior”, Journal of Consumer Psychology , 14(4): 405–415.
• Mandelbaum, E., 2011, “The architecture of belief: An essay on the unbearable automaticity of believing”, Doctoral dissertation, University of North Carolina, Carolina.
• –––, 2013, “Against alief”, Philosophical Studies , 165:197–211.
• –––, 2014, “Thinking is Believing”, Inquiry , 57(1): 55–96.
• –––, 2016, “Attitude, Association, and Inference: On the Propositional Structure of Implicit Bias”, Noûs , 50(3): 629–658.
• Mann, T. C., & M.J. Ferguson, 2017, “Reversing implicit first impressions through reinterpretation after a two-day delay,” Journal of Experimental Social Psychology , 68: 122–127.
• Mann, N. & K. Kawakami, 2012, “The long, steep path to equality: Progressing on egalitarian goals”, Journal of Experimental Psychology: General , 141(1): 187.
• McConahay, J., 1982, “Self-interest versus racial attitudes as correlates of anti-busing attitudes in Louisville: Is it the buses or the Blacks?”, The Journal of Politics , 44(3): 692–720.
• McConahay, J., B. Hardee, & V. Batts, 1981, “Has racism declined in America? It depends on who is asking and what is asked”, Journal of conflict resolution , 25(4): 563–579.
• Meissner, C. & J. Brigham, 2001, “Thirty years of investigating the own-race bias in memory for faces: A meta-analytic review”, Psychology, Public Policy, and Law , 7(1): 3–35.
• Mekawi, Y., & K. Bresin, 2015, “Is the evidence from racial bias shooting task studies a smoking gun? Results from a meta-analysis,” Journal of Experimental Social Psychology , 61: 120–130.
• Mendoza, S., P. Gollwitzer, & D. Amodio, 2010, “Reducing the Expression of Implicit Stereotypes: Reflexive Control Through Implementation Intentions”, Personality and Social Psychology Bulletin , 36(4): 512–523.
• Merleau-Ponty, M., 1945/2013, Phenomenology of Perception , New York: Routledge.
• Millikan, R., 1995, “Pushmi-pullyu representations”, Philosophical Perspectives , 9: 185–200.Mills, C., 1999, The Racial Contract , Ithaca, NY: Cornell University Press.
• Mischel, W., 1968, Personality and Assessment . Hoboken, NJ: Wiley.
• Mitchell, C., J. De Houwer, & P. Lovibond, 2009, “The propositional nature of human associative learning”, Behavioral and Brain Sciences , 32(2): 183–198.
• Monteith, M., 1993, “Self-regulation of prejudiced responses: Implications for progress in prejudice-reduction efforts”, Journal of Personality and Social Psychology , 65(3): 469–485.
• Monteith, M., L. Ashburn-Nardo, C. Voils, & A. Czopp, 2002, “Putting the brakes on prejudice: on the development and operation of cues for control”, Journal of personality and social psychology ”, 83(5): 1029–1050.
• Moskowitz, G. & P. Li, 2011, “Egalitarian goals trigger stereotype inhibition: a proactive form of stereotype control”, Journal of Experimental Social Psychology , 47(1): 103–116.
• Moss-Racusin, C., J. Dovidio, V. Brescoll, M. Graham, & J. Handelsman, 2012, “Science faculty’s subtle gender biases favor male students”, Proceedings of the National Academy of the Sciences , 109(41): 16474–16479. doi:10.1073/pnas.1211286109
• Mugg, J., 2013, “What are the cognitive costs of racism? A reply to Gendler”, Philosophical studies , 166(2): 217–229.
• Muller, H. & B. Bashour, 2011, “Why alief is not a legitimate psychological category”, Journal of Philosophical Research , 36: 371–389.
• Murphy, M.C., K.M., Kroeper, & E.M. Ozier, 2018, “Prejudiced Places: How Contexts Shape Inequality and How Policy Can Change Them,” Policy Insights from the Behavioral and Brain Sciences , 5(1): 66–74.
• Murphy, M.C. & G.M. Walton, 2013, “From prejudiced people to prejudiced places: A social-contextual approach to prejudice,” in C. Stangor & C. Crandall (eds.), Frontiers in Social Psychology Series: Stereotyping and Prejudice , Psychology Press: New York, NY.
• Nagel, J., 2012, “Gendler on alief”, Analysis , 72(4): 774–788.
• Nier, J., 2005, “How dissociated are implicit and explicit racial attitudes?: A bogus pipeline approach”, Group Processes & Intergroup Relations , 8: 39–52.
• Nisbett, R. & T. Wilson, 1977, “Telling more than we can know: Verbal reports on mental processes”, Psychological review , 84(3): 231–259.
• Nosek, B. & M. Banaji, 2001, “The go/no-go association task”, Social Cognition , 19(6): 625–666.
• Nosek, B., M. Banaji, & A. Greenwald, 2002, “Harvesting intergroup implicit attitudes and beliefs from a demonstration website”, Group Dynamics , 6: 101–115.
• Nosek, B., J. Graham, & C. Hawkins, 2010, “Implicit Political Cognition”, in Handbook of implicit social cognition: Measurement, theory, and applications , B. Gawronski & B. Payne (eds.), New York, NY: Guilford Press, pp. 548–564.
• Nosek, B., A. Greenwald, & M. Banaji, 2005, “Understanding and using the Implicit Association Test: II. Method variables and construct validity”, Personality and Social Psychology Bulletin , 31(2): 166–180.
• –––, 2007, “The Implicit Association Test at Age 7: A Methodological and Conceptual Review”, in Automatic Processes in Social Thinking and Behavior , J.A. Bargh (ed.), Philadelphia: Psychology Press.
• Nosek, B., C. Hawkins, & R. Frazier, 2011, “Implicit social cognition: from measures to mechanisms”, Trends in cognitive sciences , 15(4): 152–159.
• Olson, M. & R. Fazio, 2001, “Implicit attitude formation through classic conditioning”, Psychological Science , 12(5): 413–417.
• –––, 2006, “Reducing automatically activated racial prejudice through implicit evaluative conditioning”, Personality and Social Psychology Bulletin , 32: 421–433.
• –––, 2009, “Implicit and explicit measures of attitudes: The perspective of the MODE model”, Attitudes: Insights from the new implicit measures , 19–63.
• Oskamp, S., Harrington, M. J., Edwards, T. C., Sherwood, D. L., Okuda, S. M., & D.C. Swanson, 1991, “Factors influencing household recycling behavior,” Environment and behavior , 23(4): 494–519.
• Oswald, F., G. Mitchell, H. Blanton, J. Jaccard, & P. Tetlock, 2013, “Predicting Ethnic and Racial Discrimination: A Meta-Analysis of IAT Criterion Studies”, Journal of Personality and Social Psychology , 105(2): 171–192. doi: 10.1037/a0032734
• –––, 2015, “Using the IAT to predict ethnic and racial discrimination: Small effect sizes of unknown societal significance,” 108(4): 562–571.
• Paluck, E. & D. Green, 2009, “Prejudice Reduction: What Works? A Review and Assessment of Research and Practice”, Annual Review of Psychology , 60: 339–367.
• Payne, B., 2001, “Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon”, Journal of Personality and Social Psychology , 81: 181–192.
• Payne, B., C.M. Cheng, O. Govorun, & B. Stewart, 2005, “An inkblot for attitudes: Affect misattribution as implicit measurement”, Journal of Personality and Social Psychology , 89: 277–293.
• Payne, B., & B. Gawronski, 2010, “A history of implicit social cognition: Where is it coming from? Where is it now? Where is it going?”, in Handbook of implicit social cognition: Measurement, theory, and applications , B. Gawronski, & B. Payne (eds.), New York, NY: Guilford Press, pp. 1–17.
• Payne, B., A. Lambert, & L. Jacoby, 2002, “Best laid plans: Effects of goals on accessibility bias and cognitive control in race-based misperceptions of weapons”, Journal of Experimental Social Psychology , 38: 384–396.
• Payne, B. K., Vuletich, H. A., & K.B. Lundberg, K. B., 2017, “The bias of crowds: How implicit bias bridges personal and systemic prejudice,” Psychological Inquiry , 28: 233–248.
• Penner, L., J. Dovidio, T. West, S. Gaertner, T. Albrecht, R. Dailey, & T. Markova, 2010, “Aversive racism and medical interactions with Black patients: A field study”, Journal of Experimental Social Psychology , 46(2): 436–440.
• Perkins, A. & M. Forehand, 2012, “Implicit self-referencing: The effect of nonvolitional self-association on brand and product attitude”, Journal of Consumer Research , 39(1): 142–156.
• Pessoa, L., forthcoming, “The Cognitive-Emotional Brain”, Behavioral and Brain Sciences .
• Peters, D. & S. Ceci, 1982, “Peer-review practices of psychological journals: The fate of published articles, submitted again”, Behavioral and Brain Sciences , 5(2): 187–195.
• Pettigrew, T. & L. Tropp, 2006, “A Meta-Analytic Test of Intergroup Contact Theory”, Journal of Personality and Social Psychology , 90: 751–83.
• Petty, R., 2006, “A metacognitive model of attitudes”, Journal of Consumer Research , 33(1): 22–24.
• Petty, R., P. Briñol, & K. DeMarree, 2007, “The meta-cognitive model (MCM) of attitudes: Implications for attitude measurement, change, and strength”, Social Cognition , 25(5): 657–686.
• Phills, C., K. Kawakami, E. Tabi, D. Nadolny, & M. Inzlicht, 2011, “Mind the Gap: Increasing the associations between the self and blacks with approach behaviors”, Journal of Personality and Social Psychology , 100: 197–210.
• Proffitt, D., 2006, “Embodied perception and the economy of action”, Perspectives on psychological science , 1(2): 110–122.
• Puddifoot, K., 2017, “Dissolving the epistemic/ethical dilemma over implicit bias,” Philosophical Explorations , 20(sup1): 73–93.
• Railton, P., 2009, “Practical Competence and Fluent Agency”, in Reasons for Action , D. Sobel & S. Wall (eds.), Cambridge: Cambridge University Press, pp. 81–115.
• –––, 2014, “The Affective Dog and its Rational Tale: Intuition and Attunement”, Ethics , 124(4): 813–859.
• Richeson, J. & J. Shelton, 2003, “When prejudice does not pay effects of interracial contact on executive function”, Psychological Science , 14(3): 287–290.
• –––, 2007, “Negotiating interracial interactions: Costs, consequences, and possibilities”, Current Directions in Psychological Science , 16: 316–320.
• Ross, L., M. Lepper, & M. Hubbard, 1975, “Perseverance in Self-Perception and Social Perception: Biased Attributional Processes in the Debriefing Paradigm”, Journal of Personality and Social Psychology , 32(5): 880–802.
• Ryle, G., 1949/2009, The Concept of Mind , New York: Routledge.
• Sarkissian, H., 2010, “Minor tweaks, major payoffs: The problems and promise of situationalism in moral philosophy”, Philosopher’s Imprint , 10(9): 1–15.
• Saul, J., 2012, “Skepticism and Implicit Bias”, Disputatio , Lecture, 5(37): 243–263.
• –––, 2013, “Unconscious Influences and Women in Philosophy”, in Women in Philosophy: What Needs to Change?, F. Jenkins & K. Hutchison (eds.), Oxford: Oxford University Press.
• Scanlon, T., 1998, What We Owe Each Other , Cambridge: Harvard University Press.
• Schacter, D., 1987, “Implicit memory: History and current status”, Journal of Experimental Psychology: Learning, Memory, and Cognition , 13: 501–518.
• Schneider, D., 2004, The Psychology of Stereotyping , New York: Guilford Press.
• Schwitzgebel, E., 2002, “A Phenomenal, Dispositional Account of Belief”, Nous , 36: 249–275.
• –––, 2006/2010, “Belief”, The Stanford Encyclopedia of Philosophy , (Winter 2010 edition), E. Zalta (ed.), URL = < https://plato.stanford.edu/archives/win2010/entries/belief/ >
• –––, 2010, “Acting contrary to our professed beliefs, or the gulf between occurrent judgment and dispositional belief”, Pacific Philosophical Quarterly , 91: 531–553.
• –––, 2013, “A Dispositional Approach to Attitudes: Thinking Outside of the Belief Box”, in New Essays on Belief , N. Nottelmann (ed.), New York: Palgrave Macmillan, pp. 75–99.
• Sher, G., 2009, Who Knew? Responsibility without Awareness , Oxford: Oxford University Press.
• Shiffrin, R. & W. Schneider, 1977, “Controlled and automatic human information processing: Perceptual learning, automatic attending, and a general theory”, Psychological Review , 84: 127–190.
• Shoemaker, D., 2003, “Caring, Identification, and Agency”, Ethics , 118: 88–118.
• –––, 2011, “Attributability, Answerability, and Accountability: Towards a Wider Theory of Moral Responsibility”, Ethics , 121: 602–632.
• Sie, M. & N. Vorst Vader-Bours, 2016, “Personal Responsibility vis-à-vis Prejudice Resulting from Implicit Bias”, in Brownstein & Saul (eds.) 2016B.
• Siegel, S., 2012, “Cognitive Penetrability and Perceptual Justification”, Nous , 46(2): 201–222.
• –––, 2017, The Rationality of Perception , Oxford: Oxford University Press.
• –––, forthcoming, “Bias and Perception, in Beeghly & Madva (eds.), forthcoming.
• Singal, J., 2017, “Psychology’s Favorite Tool for Measuring Racism isn’t Up to the Job,” New York Magazine , [ available online ].
• Smith, A., 2005, “Responsibility for attitudes: activity and passivity in mental life”, Ethics , 115(2): 236–271.
• –––, 2008, “Control, responsibility, and moral assessment”, Philosophical Studies ,138: 367–392.
• –––, 2012, “Attributability, Answerability, and Accountability: In Defense of a Unified Account”, Ethics , 122(3): 575–589.
• Smith, H., 2011, “Non-Tracing Cases of Culpable Ignorance”, Criminal Law and Philosophy , 5: 115–146.
• Snow, N., 2006, “Habitual Virtuous Actions and Automaticity”, Ethical Theory and Moral Practice , 9: 545–561.
• Sripada, C., 2016, “Self-expression: A deep self theory of moral responsibility,” Philosophical Studies , 173(5): 1203–1232.
• Stalnaker, R., 1984, Inquiry , Cambridge, MA: MIT Press.
• Steele, C. & J. Aronson, 1995, “Stereotype threat and the intellectual test performance of African Americans”, Journal of personality and social psychology , 69(5): 797–811.
• Stewart, B. & B. Payne, 2008, “Bringing Automatic Stereotyping under Control: Implementation Intentions as Efficient Means of Thought Control”, Personality and Social Psychology Bulletin , 34: 1332–1345.
• Strack, F. & R. Deutsch, 2004, “Reflective and impulsive determinants of social behaviour”, Personality and Social Psychology Review , 8: 220–247.Strenze, T., 2007, “Intelligence and socioeconomic success: A meta-analytic review of longitudinal research ,” Intelligence, 35: 401–426.
• Suhler, C. & P. Churchland, 2009, “Control: conscious and otherwise”, Trends in cognitive sciences , 13(8): 341–347.
• Talaska, C., Fiske, S., and S. Chaiken, 2008, “Legitimating Racial Discrimination: Emotions, Not Beliefs, Best Predict Discrimination in a Meta-Analysis,” Social Justice Research 21(3): 263–296.
• Taylor, S. & J. Brown, 1988, “Illusion and well-being: a social psychological perspective on mental health”, Psychological bulletin , 103(2): 193–210.
• Teachman, B. A., & S.R. Woody, S. R., 2003, “Automatic processing in spider phobia: Implicit fear associations over the course of treatment,” Journal of Abnormal Psychology , 112(1): 100.
• Tetlock, P., O. Kristel, B. Elson, M. Green, & J. Lerner, 2000, “The psychology of the unthinkable: Taboo trade-offs, forbidden base rates, and heretical counterfactuals”, Journal of Personality and Social Psychology , 78(5): 853–870.
• Tetlock, P., & G. Mitchell, 2009, “Implicit bias and accountability systems: What must organizations do to prevent discrimination?”, Research in Organizational Behavior , 29: 3–38.
• Trawalter, S. & J. Richeson, 2006, “Regulatory focus and executive function after interracial interactions”, Journal of Experimental Social Psychology , 42(3): 406–412.
• Valian, V., 1998, Why so slow? The advancement of women , Cambridge, MA: M.I.T. Press.
• –––, 2005, “Beyond gender schemas: Improving the advancement of women in academia”, Hypatia , 20: 198–213.
• Van Dessel, P., De Houwer, J., & C.T. Smith, 2018, “Relational information moderates approach-avoidance instruction effects on implicit evaluation,” Acta Psychologica , 184: 137–143.
• Vargas, M., 2005, “The Revisionist’s Guide to Responsibility”, Philosophical Studies , 125(3): 399–429.Vianello, M., Robusto, E., & P. Anselmi, 2010, “Implicit conscientiousness predicts academic performance,” Personality and Individual Differences, 48: 452–457.
• Walther, E., 2002, “Guilty by Mere Association: Evaluative Conditioning and the Spreading Attitude Effect”, Journal of Personality and Social Psychology , 82(6): 919–34.
• Washington, N. & D. Kelly, 2016, “Who’s responsible for this? Implicit bias and the knowledge condition”, in Brownstein & Saul (eds.) 2016B.
• Watson, G., 1975, “Free Agency”, Journal of Philosophy , 72(8): 205–220.
• –––, 1996, “Two faces of responsibility”, Philosophical Topics , 24(2): 227–248.
• Webb, T., P. Sheeran, & A. Pepper, 2012, “Gaining control over responses to implicit attitude tests: Implementation intentions engender fast responses on attitude-incongruent trials”, British Journal of Social Psychology , 51(1): 13–32. doi:10.1348/014466610X532192
• Welpinghus, A., forthcoming, “The imagination model of implicit bias,” Philosophical Studies , first online 11 March 2019. doi:10.1007/s11098-019-01277-1
• Wigley, S., 2007, “Automaticity, Consciousness, and Moral Responsibility”, Philosophical Psychology , 20(2): 209–225.
• Wolfe, R., & S. Johnson, 1995, “Personality as a Predictor of College Performance,” Education and Psychological Measurement, 55(2): 177–185.
• Zanna, M. P., & R.H. Fazio, 1982, “The attitude-behavior relation: Moving toward a third generation of research,” in M. P. Zanna, E. T. Higgins, C. P. Herman (Eds.), Consistency in social behavior: The Ontario symposium (Vol. 2, pp. 283–301), Hillsdale, N.J.: Erlbaum.
• Zeigler-Hill, V. & C. Jordan, 2010, “Two faces of self-esteem”, in Handbook of implicit social cognition: Measurement, theory, and applications , B. Gawronski & B. Payne (eds.), NY: Guilford Press, pp. 392–407.
• Zheng, R., 2016, “Attributability, Accountability and Implicit Attitudes”, in Brownstein & Saul (eds.) 2016B.
• Zimmerman, A., 2007, “The nature of belief”, Journal of Consciousness Studies , 14(11): 61–82.

How to cite this entry . Preview the PDF version of this entry at the Friends of the SEP Society . Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO). Enhanced bibliography for this entry at PhilPapers , with links to its database.

Other Internet Resources

• Brownstein, M., Madva, A., and B. Gawronski, ms., “Understanding implicit bias: Putting the criticism into perspective” .
• Johnson, G., ms, “The Structure of Bias”.
• Mallon, R., ms, “Psychology, Accumulation Mechanisms, and Race”.
• Hermanson, S., 2018, “Rethinking Implicit Bias: I want my money back,” [ available online ].
• Jussim, L., 2018, “Comment on Hermanson, S., 2018, Rethinking Implicit Bias: I want my money back,” [ available online ].
• Project Implicit (homepage of the IAT)
• Climate for Women and Underrepresented Groups at Rutgers
• MAP (Minorities and Philosophy)
• Active Bystander Strategies
• Tutorials for Change—Gender Schemas and Science
• The Gender Equity Project
• Philosophy of Brains Roundtable on the IAT
• Peanut Butter, Jelly and Racism

belief | cognitive science | feminist philosophy, interventions: moral psychology | feminist philosophy, interventions: social epistemology | moral responsibility | race | self-knowledge

Acknowledgments

Many thanks to Yarrow Dunham, Jules Holroyd, Bryce Huebner, Daniel Kelly, Calvin Lai, Carole Lee, Alex Madva, Eric Mandelbaum, Jennifer Saul, and Susanna Siegel for invaluable suggestions and feedback. Thanks also to the Leverhulme Trust for funding the “Implicit Bias and Philosophy” workshops at the University of Sheffield from 2011–2013, and to Jennifer Saul for running the workshops and making them a model of scholarship and collaboration at its best.

Copyright © 2019 by Michael Brownstein < msbrownstein @ gmail . com >

• Accessibility

Support SEP

Mirror sites.

View this site from another server:

• Info about mirror sites

The Stanford Encyclopedia of Philosophy is copyright © 2023 by The Metaphysics Research Lab , Department of Philosophy, Stanford University

Library of Congress Catalog Data: ISSN 1095-5054

What do you think? Leave a respectful comment.

Betsy Mason, Knowable Magazine Betsy Mason, Knowable Magazine

• Copy URL https://www.pbs.org/newshour/nation/making-people-aware-of-their-implicit-biases-doesnt-usually-change-minds-but-heres-what-does-work

Making people aware of their implicit biases doesn’t usually change minds. But here’s what does work

This interview with psychologist Anthony Greenwald was republished with permission from Knowable Magazine. The original article was published on June 4, 2020.

A quarter-century ago, social psychologist Anthony Greenwald of the University of Washington developed a test that exposed an uncomfortable aspect of the human mind: People have deep-seated biases of which they are completely unaware. And these hidden attitudes — known as implicit bias — influence the way we act toward each other, often with unintended discriminatory consequences.

Since then, Greenwald and his main collaborators, Mahzarin Banaji and Brian Nosek, have used the implicit association test to measure how fast and accurately people associate different social groups with qualities like good and bad. They have developed versions of the test to measure things such as unconscious attitudes about race, gender stereotypes and bias against older people. Those tests have revealed just how pervasive implicit bias is. (Project Implicit offers public versions of the tests on its website here .)

The researchers’ work has also shown how much implicit bias can shape social behavior and decision-making. Even people with the best intentions are influenced by these hidden attitudes, behaving in ways that can create disparities in hiring practices , student evaluations , law enforcement , criminal proceedings — pretty much anywhere people are making decisions that affect others. Such disparities can result from bias against certain groups, or favoritism toward other ones. Today, implicit bias is widely understood to be a cause of unintended discrimination that leads to racial, ethnic, socioeconomic and other inequalities.

Discussions around the role of racism and implicit bias in the pattern of unequal treatment of racial minorities by law enforcement are intensifying following a roster of high-profile cases, most recently the killing of George Floyd. Floyd, an unarmed black man, died in Minneapolis last month after a white police officer pressed his knee into Floyd’s neck for nearly nine minutes.

As awareness of implicit bias and its effects has increased, so has interest in mitigating it. But that is much harder to do than scientists expected, as Greenwald told an audience in Seattle in February at the annual meeting of the American Association for the Advancement of Science. Greenwald, coauthor of an overview on implicit bias research in the 2020 Annual Review of Psychology , spoke with Knowable Magazine about what does and doesn’t work to counter the disparities that implicit bias can produce.

This conversation has been edited for length and clarity.

How do you test for associations that people aren’t aware they have?

The first implicit association test I created was one involving the names of flowers and insects, and words meaning things pleasant or unpleasant. You had to use left and right hands to classify them, tapping on a keyboard as they appeared on the screen. It was a very easy task when you had to use the right hand for both pleasant words and flower names, and the left hand for unpleasant words and insect names, because we typically think of flowers as pleasant and insects as unpleasant.

But then the task is switched to force the opposite associations — one hand for insect names and pleasant words, and the other hand for flower names and unpleasant words. When I first tried that reversed form, my response time was about a third of a second slower compared to the first version. And in psychological work where you’re asking people to respond rapidly, a third of a second is like an eternity, indicating that some mental processes are going on in this version of the test that are not going on in the other.

Then I replaced the flowers and insects with first names of men and women that are easily classified as European American or African American. For me, giving the same response to pleasant words and African American names took an eternity. But when it was the European American names and pleasant words with one hand, and the African American names and the unpleasant words with the other hand, that was something I could zip through. And that was a surprise to me. I would have described myself at that point as someone who is lacking in biases or prejudices of a racial nature. I probably had some biases that I would confess to, but I actually didn’t think I had that one.

How widespread is implicit bias?

That particular implicit bias, the one involving black-white race, shows up in about 70 percent to 75 percent of all Americans who try the test. It shows up more strongly in white Americans and Asian Americans than in mixed-race or African Americans. African Americans, you’d think, might show just the reverse effect — that it would be easy for them to put African American together with pleasant and white American together with unpleasant. But no, African Americans show, on average, neither direction of bias on that task.

Most people have multiple implicit biases they aren’t aware of. It is much more widespread than is generally assumed.

Is implicit bias a factor in the pattern of police violence such as that seen in the killing of George Floyd on May 25, which sparked the ongoing protests across the country?

The problems surfacing in the wake of George Floyd’s death include all forms of bias, ranging from implicit bias to structural bias built into the operation of police departments, courts and governments, to explicit, intended bias, to hate crime.

The best theory of how implicit bias works is that it shapes conscious thought, which in turn guides judgments and decisions. The ABC News correspondent Pierre Thomas expressed this very well recently by saying, “Black people feel like they are being treated as suspects first and citizens second.” When a black person does something that is open to alternative interpretations, like reaching into a pocket or a car’s glove compartment, many people — not just police officers — may think first that it’s possibly dangerous. But that wouldn’t happen in viewing a white person do exactly the same action. The implications of conscious judgment being shaped in this way by an automatic, implicit process of which the perceiver is unaware can assume great importance in outcomes of interactions with police.

Do the diversity or implicit bias training programs used by companies and institutions like Starbucks and the Oakland Police Department help reduce bias?

I’m at the moment very skeptical about most of what’s offered under the label of implicit bias training, because the methods being used have not been tested scientifically to indicate that they are effective. And they’re using it without trying to assess whether the training they do is achieving the desired results.

I see most implicit bias training as window dressing that looks good both internally to an organization and externally, as if you’re concerned and trying to do something. But it can be deployed without actually achieving anything , which makes it in fact counterproductive. After 10 years of doing this stuff and nobody reporting data, I think the logical conclusion is that if it was working, we would have heard about it.

Can you tell us about some of the approaches meant to reduce bias that haven’t worked?

I’ll give you several examples of techniques that have been tried with the assumption that they would achieve what’s sometimes called debiasing or reducing implicit biases. One is exposure to counter-stereotypic examples, like seeing examples of admirable scientists or entertainers or others who are African American alongside examples of whites who are mass murderers. And that produces an immediate effect. You can show that it will actually affect a test result if you measure it within about a half-hour. But it was recently found that when people started to do these tests with longer delays, a day or more, any beneficial effect appears to be gone .

Other strategies that haven’t been very effective include just encouraging people to have a strong intention not to allow themselves to be biased. Or trainers will suggest people do something that they may call “thinking slow” or pausing before making decisions. Another method that has been tried is meditation. And another strategy is making people aware that they have implicit biases or that implicit biases are pervasive in the population. All these may seem reasonable, but there’s no empirical demonstration that they work.

It’s surprising to me that making people aware of their bias doesn’t do anything to mitigate it. Why do you think that is?

I think you’re right, it is surprising. The mechanisms by which our brains form associations and acquire them from the cultural environment evolved over long periods of time, during which people lived in an environment that was consistent. They were not actually likely to acquire something that they would later have to unlearn, because the environment wasn’t going to change. So there may have been no evolutionary pressure for the human brain to develop a method of unlearning the associations.

I don’t know why we have not succeeded in developing effective techniques to reduce implicit biases as they are measured by the implicit association test. I’m not prepared to say that we’re never going to be able to do it, but I will say that people have been looking for a long time, ever since the test was introduced, which is over 20 years now, and this hasn’t been solved yet.

Is there anything that does work?

I think that a lot can be achieved just by collecting data to document disparities that are occurring as a result of bias. And maybe an easy example is police operations, although it can be applied in many settings. Most police departments keep data on what we know as profiling, though they don’t like to call it that. It’s what happens in a traffic stop or a pedestrian stop — for example, the stop-and-frisk policy that former New York City Mayor Michael Bloomberg has taken heat for. The data of the New York City Police Department for stops of black and white pedestrians and drivers were analyzed, and it was very clear that there were disparities.

Once you know where the problem is that has to be solved, it’s up to the administrators to figure out ways to understand why and how this is happening. Is it happening in just some parts of the city? Is it that the police are just operating more in Harlem than in the white neighborhoods?

And once you know what’s happening, the next step is what I call discretion elimination. This can be applied when people are making decisions that involve subjective judgment about a person. This could be police officers, employers making hiring or promotion decisions, doctors deciding on a patient’s treatment, or teachers making decisions about students’ performance. When those decisions are made with discretion, they are likely to result in unintended disparities. But when those decisions are made based on predetermined, objective criteria that are rigorously applied, they are much less likely to produce disparities.

Is there evidence that discretion elimination works?

What we know comes from the rare occasions in which the effects of discretion elimination have been recorded and reported. The classic example of this is when major symphony orchestras in the United States started using blind auditions in the 1970s. This was originally done because musicians thought that the auditions were biased in favor of graduates of certain schools like the Juilliard School. They weren’t concerned about gender discrimination.

But as soon as auditions started to be made behind screens so the performer could not be seen, the share of women hired as instrumentalists in major symphony orchestras rose from around 10 percent or 20 percent before 1970 to about 40 percent. This has had a major impact on the rate at which women have become instrumentalists in major symphony orchestras.

But these data-collection and discretion-elimination strategies aren’t commonly used?

Not nearly as often as they could. For example, instructors can usually arrange to grade almost anything that a student does without knowing the identity of the student. In an electronic age when you don’t learn to recognize people’s handwriting, instructors can grade essays without the students’ names on them. I used that approach when I was last grading undergraduates in courses. It’s easy to use, but it’s often not used at all.

And in many other circumstances it is possible to evaluate performance without knowing the identity of the person being evaluated. But employers and others rarely forgo the opportunity to know the identity of the person they’re evaluating.

Can artificial intelligence play a role?

People are starting to apply artificial intelligence to the task by mining historical records of past employment decisions. This is a way of taking the decisions that involve human discretion and putting them into the hands of a machine. The idea is to develop algorithms that identify promising applicants by matching their qualities to those of past applicants who turned out to be successful employees.

I think it’s a great thing to try. But so far, efforts with AI have not succeeded, because the historical databases used to develop the algorithms to make these decisions turn out to be biased, too. They incorporate the biases of past decision-makers. One example is how biases affect facial-recognition technology , which inadvertently categorizes African American faces or Asian faces as criminal more often than white faces.

This is a problem that computer scientists are trying to cope with, but some of the people in AI that I have spoken to seem not so optimistic that this will be at all easy to do. But I do think that ultimately — and it might take a while — the biases may be expunged more readily from AI decision algorithms than from human decision-making.

Could more be done at the level of an individual company or department?

To help prevent unintended discrimination, the leaders of organizations need to decide to track data to see where disparities are occurring. When they discover disparities, they need to try to make changes and then look at the next cycle of data to see if those changes are improving things.

Obviously, it’s easier for them not to do those things. In some cases there’s a cost to doing them. And they may think it’s like opening up Pandora’s box if they look closely at the data. I think this is true of many police departments. They’re bound to find things that they’d rather not see.

This article originally appeared in Knowable Magazine , an independent journalistic endeavor from Annual Reviews. Sign up for the newsletter .

Betsy Mason is a freelance journalist based in the San Francisco Bay Area who specializes in science and cartography.

Support Provided By: Learn more

Educate your inbox

Subscribe to Here’s the Deal, our politics newsletter for analysis you won’t find anywhere else.

Thank you. Please check your inbox to confirm.

March 27, 2018

How to Think about ‘Implicit Bias’

Amid a controversy, it’s important to remember that implicit bias is real—and it matters

By Keith Payne , Laura Niemi & John M. Doris

Lyubov Ivanova Getty Images

When’s the last time a stereotype popped into your mind? If you are like most people, the authors included, it happens all the time. That doesn’t make you a racist, sexist or whatever-ist. It means your brain is noticing patterns and making generalizations. But the same thought processes that make people smart can also make them biased. This tendency for stereotype-confirming thoughts to pass spontaneously through our minds is what psychologists call implicit bias. It sets people up to overgeneralize, sometimes leading to discrimination even when people feel they are being fair.

Studies of implicit bias have drawn ire from both the right and the left. For the right, talk of implicit bias is just another instance of progressives seeing injustice under every bush. For the left, implicit bias diverts attention from more damaging instances of explicit bigotry. Debates have become heated and have leaped from scientific journals to the popular press. Along the way, some important points have been lost. We highlight two misunderstandings that anyone who wants to understand implicit bias should know about.

First, much of the controversy centers on the most famous implicit bias test, the Implicit Association Test (IAT). A majority of people taking this test show evidence of implicit bias, suggesting that most individuals are implicitly biased even if they do not think of themselves as prejudiced. As with any measure, the test does have limitations. The stability of the test is low, meaning that if you take the same test a few weeks apart, you might score very differently. And the correlation between a person’s IAT scores and discriminatory behavior is often small.

On supporting science journalism

If you're enjoying this article, consider supporting our award-winning journalism by subscribing . By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.

The IAT is a measure, and it doesn’t follow from a particular measure being flawed that the phenomenon we are attempting to measure is not real. Drawing that conclusion is to commit the Divining Rod Fallacy: just because a rod doesn’t find water doesn’t mean there’s no such thing as water. A smarter move is to ask, “What does the other evidence show?”

In fact, there is lots of other evidence. There are perceptual illusions, for example, in which white subjects perceive Black faces as angrier than white faces with the same expression. Bias can cause people to see harmless objects as weapons when they are in the hands of Black men and to dislike abstract images that are paired with Black faces. And there are dozens of variants of laboratory tasks finding that most participants are faster to identify bad words paired with Black faces than with white faces. None of these measures is without limitations, but each shows the same pattern of reliable bias as the IAT. There is a mountain of evidence—independent of any single test—that implicit bias is real.

The second misunderstanding is about what scientists mean when they say a measure predicts behavior. One frequent complaint is that an individual’s IAT score doesn’t tell you whether the person will discriminate on a particular occasion. This is to commit the Palm Reading Fallacy: unlike palm readers, research psychologists aren’t usually in the business of telling you, as an individual, what your life holds in store. Most measures in psychology, from aptitude tests to personality scales, are useful for predicting how groups will respond on average , not forecasting how particular individuals will behave.

The difference is crucial. Knowing that an employee scored high on conscientiousness won’t tell you much about whether their work will be careful or sloppy if you inspect it right now. But if a large company hires hundreds of employees who are all conscientious, this will likely pay off with a small but consistent increase in careful work on average.

Implicit bias researchers have always warned against using the tests for predicting individual outcomes, such as how a particular manager will behave in job interviews—they’ve never been in the palm-reading business. What the IAT does, and does well, is predict average outcomes across larger entities such as counties, cities or states. For example, metro areas with greater average implicit bias have larger racial disparities in police shootings. And counties with greater average implicit bias have larger racial disparities in infant health problems. These correlations are important: the lives of Black citizens and newborn Black babies depend on them.

Field experiments demonstrate that real-world discrimination continues and is widespread. White applicants get about 50 percent more callbacks than Black applicants with the same résumés; college professors are 26 percent more likely to respond to a student’s e-mail when it is signed by Brad rather than Lamar; and physicians recommend less pain medication for Black patients than for white patients with the same injury.

Today managers are unlikely to announce that white job applicants should be chosen over Black applicants, and physicians don’t declare that Black people feel less pain than white people. Yet the broad pattern of discrimination and disparities seen in field studies persists. It bears a much closer resemblance to the widespread stereotypical thoughts seen on implicit bias tests than to results of survey studies in which most people present themselves as unbiased.

One reason people on both the right and the left are skeptical of implicit bias might be pretty simple: it isn’t nice to think we aren’t very nice. It would be comforting to conclude, when we don’t consciously entertain impure intentions, that all of our intentions are pure. Unfortunately, we can’t conclude that: many of us are more biased than we realize. And that is an important cause of injustice—whether you know it or not.

Keith Payne is a professor in psychology and neuroscience at the University of North Carolina at Chapel Hill. He is author of The Broken Ladder: How Inequality Affects the Way We Think, Live, and Die (Viking, 2017).

Laura Niemi is an assistant professor in the department of psychology at Cornell University. She researches moral judgment and the implications of differences in moral values.

John M. Doris is Peter L. Dyson Professor of Ethics in Organizations and Life at the Charles H. Dyson School of Applied Economics and Management and a professor at the Sage School of Philosophy at Cornell University.

Everyone has unconscious biases — here's how to identify, address, and overcome them

• Implicit bias refers to unconscious stereotypes against others and how they affect our behavior.
• Implicit bias, aka unconscious bias, reinforces inequalities at work, school, the doctor's, and more.
• It's possible to overcome implicit bias with training and policy changes upheld by organizations.

Implicit bias , also known as unconscious bias, refers to having a preference for, aversion to, or stereotypes about a certain group of people on an unconscious level. Unlike racism or sexism — a conscious discrimination against a group of people — people with implicit biases are often not aware of the ways that their biases affect their behavior. 

"Although we like to imagine that we are fair, reasonable and unbiased in the small and big choices and decisions we make in our daily lives, we are neither truly impartial nor neutral," says Tanya Mathew , co-chair of Cultural Competency & Anti-Bias Education at the Diversity Council at The Ohio State University Wexner Medical Center .

Implicit biases can reinforce existing inequities and stereotypes, Matthew says. You're not a bad person for having implicit biases, but it's important to acknowledge that these feelings can and have had a significant impact on the workplace, medicine, education, and more.

"It insidiously and automatically seeps into a person's behavior, and is outside of the full awareness of that person," she says. "And yet it's shaping decisions made and how they evaluate and interact with people."  

One of the most powerful ways to combat implicit biases is to be more conscious of perceptions and actively push back on them, says Mathew. Keep reading to learn how to become more fair and unbiased by bringing your unconscious biases into your awareness.

What is implicit bias

The concept of implicit bias was introduced in 1995 by research psychologists Mahzarin Banaji and Anthony Greenwald. They argued that while most people think social interactions are under conscious control, they are actually heavily influenced by implicit, unconscious biases.

Since then, the concept has been scientifically proven using brain scans, says Mathew. For example, a 2018 study found people's brains reacted differently to images of people they perceived as similar to them, versus people they preserved as dissimilar. 

People usually associate implicit bias with race, but it can be based on virtually any factor, including body size, speech patterns, age, income, sexual orientation, or hair color, says Mathew.  

An easy way to identify some of your implicit biases can be simply asking yourself what type of person you picture when you hear the following:

• Nurse 
• Police officer

Examples of implicit bias

Everyone has implicit biases that we act on unintentionally , says Nathaniel Ivers , Ph.D, the department chairman and an associate professor of counseling at Wake Forest University . 

Here are some common examples of how implicit bias can impact the workplace, healthcare, education, and your daily life.

Implicit bias in the workplace

• About 30% of CEOs are taller than 6'2", compared with 4% of American males. This suggests an unconscious bias associating height with success. 
• Another survey of CEOs revealed that the deeper their voice, the more they were likely to earn. This suggests a preference for deep voices in male CEOs. 

Implicit bias in medicine

• Women's pain is often perceived as less severe , or downplayed, suggesting a biased belief that women are exaggerating pain. 
• Black women with fibroids are more likely than white women to have invasive treatments like a hysterectomy, suggesting a bias against working with the patient for less invasive treatment.

Implicit bias in education

• Teachers' perceptions of their students' abilities can influence actual student outcomes. For example, in districts where teachers show more pro-white bias, there is a larger achievement gap between Black and white students.
• Black students are disproportionately disciplined compared to white students . For example, Black students are more likely than white students to be suspended for the same offense. This suggests a bias against Black students over white ones. 

Implicit bias in daily life

• People, including children, may judge overweight people more harshly than thin people. 
• Parents may have lower expectations of math skills for girls compared to boys. 

Quick Tip: Get to know your biases using this test from Project Implicit, out of Harvard. "Your results do not mean that you have discriminated against others — it simply means that your unconscious mind finds it easy to make certain associations that may reveal preferences," Matthew says.

Implicit bias training

These days, more employers, educational institutions, and other organizations are providing implicit bias training to help make people more aware of their unconscious biases. 

"Understanding what implicit biases are, how they can arise and how to recognize them in yourself and others are all incredibly important in working towards overcoming such biases," Matthew says. 

Overcoming implicit bias, however, is not easy because it's an inherent reaction to distance yourself from others rather than engage. Therefore, relearning and reshaping your behavior is always a challenge, which may explain why bias training programs may not be enough . A 2019 scientific analysis found many implicit bias programs didn't change behavior. Even companies that host diversity trainings often show biases in how they perceive the qualifications of Black candidates. 

Moreover, an analysis from the Harvard Business Review found that in order to be effective, implicit bias training needs to do more than just point out the problem. Because just knowing the problem won't be helpful when people are under hard deadlines and rely more on knee-jerk reactions and first impressions.

To overcome implicit bias you must make people understand that although these biases are unconscious, they have the power to change them. It must be paired with policy changes at the organization, such as changes to family leave policies or hiring processes. 

"This has to be an organizational priority at all levels with tangible investment and support from senior leadership," says Matthew. "Otherwise it will be another performative action without real outcomes."

Breaking down biases at home

Implicit bias training is best done in a group and led by a professional, says Matthew. She recommends the virtual bias training led by Cook Ross . 

In addition to undergoing training, people can continue their education at home. 

"Mitigation has to become a daily practice and skill that needs to be continually developed," she says. 

To fight your implicit bias, Mathew recommends these steps:

• Acknowledge your biases by taking the test from Harvard. Be sure to acknowledge all your biases, even those seemingly based around positivity like a belief that women are just more nurturing than men. 
• Interact with people who trigger your biases. Maintain a diverse group of friends, and follow people of various abilities, body types, and living situations on social media. 
• Challenge your biases. For example, invite your nephew (instead of your niece) to bake with you, or take a fitness class led by an instructor who is more on the chubby side. 

In addition, Matthew recommends these books:

• " Biased: Uncovering the Hidden Prejudice That Shapes What We See Think and Do ," by Jennifer Eberhardt
• " Blindspot: Hidden Biases of Good People " by Anthony Greenwald and Mahzarin Banaji
• " Caste: The Origins of Our Discontents " by Isabel Wilkerson
• " Implicit Racial Bias Across the Law " by Justin Levinson and Robert Smith
• " Thinking, Fast and Slow " by Daniel Kahneman
• " The Sum of Us " by Heather McGhee

Implicit bias vs. explicit bias

Whereas implicit bias is unconscious, explicit bias is a behavior or belief that is consciously controlled. 

"Explicit biases are intentionally discriminative, overt and include intentional choices of words, behaviors, and actions," says Matthew. "The person is very clear about his or her feelings and attitudes, and related behaviors are conducted with intent."

Left unchecked, unconscious biases can lead to explicit bias, says Ivers. ​​ 

"There is a correlation between implicit bias and explicit bias," he says. "Unfavorable or overly favorable unconscious beliefs or attitudes about one group may lead to [conscious] unfair treatment of these groups."

With work, it's possible to overcome explicit bias just like how you can conquer implicit bias. 

Insider's takeaway

Implicit bias is a fact of life. While there's no way to rid yourself of implicit biases completely, implicit bias training can make you more aware of the ways unconscious assumptions impact your decision making and choices. 

You're not controlled by these unconscious thoughts and beliefs and by simply being more curious about them can make a big difference. Once you've identified an implicit bias ask yourself how they are affecting your choices in ways that you don't want them to. Then you'll have the necessary information to make significant changes.

"Having an unconscious bias doesn't make you a bad person — it just means you're human," says Matthew. "Thankfully, human beings have an incredible capacity to learn, grow, change, adapt and improve themselves, but only if they want to."

We may receive a commission when you buy through our links, but our reporting and recommendations are always independent and objective.

• Main content

• Our Mission

A Look at Implicit Bias and Microaggressions

A primer on the impact of implicit biases in schools and how they can be expressed by students and faculty.

Like everybody else, I possess unconscious biases about people that are contingent on how they talk and look. Such instant judgments, called implicit bias , involve “automatically categorizing people according to cultural stereotypes,” Sandra Graham and Brian Lowery write in “ Priming Unconscious Racial Stereotypes About Adolescent Offenders .”

The consequences of implicit bias in schools are both powerful and measurable. A 2017 study by Hua-Yu Sebastian Cherng, for example, found that “math teachers perceive their classes to be too difficult for Latino and black students, and English teachers perceive their classes to be too difficult for all non-white students.” In English, these biases lower the affected students’ “expected years of schooling by almost a third of a year.... The effect of being underestimated by math teachers is −0.20 GPA points.”

Implicit bias also leads to inequitable punishments for students of color. A 2012 investigation found that “17 percent, or one out of every six black schoolchildren enrolled in K–12, were suspended at least once,” compared with “one in 20 (5 percent) for whites.” Black girls ages 5 to 14 have been viewed by adults as “less innocent” than white girls of the same age, which may be a factor in the disparity in suspension rates, according to a 2017 report by Georgetown Law’s Center on Poverty and Inequality.

Implicit Bias and Microaggressions

Microaggressions are one outgrowth of implicit bias. Columbia University’s Derald Wing Sue defines this term as “prejudices that leak out in many interpersonal situations and decision points”; they are experienced as “slights, insults, indignities, and denigrating messages.”

In a 2007 article for American Psychologist , Sue and six other researchers identified three categories of racial microaggressions:

• A microassault is a “verbal or nonverbal attack meant to hurt the intended victim through name-calling, avoidant behavior, or purposeful discriminatory actions.” Example: Students wear Confederate flag clothing.
• A microinsult is insensitive communication that demeans someone’s racial identity, signaling to people of color that “their contributions are unimportant.” Example: A teacher corrects the grammar only of Hispanic children.
• A microinvalidation involves negating or ignoring the “psychological thoughts, feelings, or experiential reality of a person of color.” Example: An Asian American student from the U.S. is asked where she was born, which conveys the message that she is not really an American.

Over the years, the concept has been extended beyond race to include similar events and experiences of other marginalized groups, including women, LGBTQ people, people with disabilities, etc.

In schools, students report that experiences like these are fairly common:

• “In high school, boys in my math classes would look over my shoulder and unsolicited point out my errors with their pencils.”
• “Sometimes I’m asked, ‘Why are you so white?’ meaning that people with Arab names and heritage are supposed to be all dark-skinned, and I’m asked to justify my skin color and explain why I don’t match their racial stereotypes.”
• “I’ve been told, ‘Go back to Mexico!’ many times.”

Other microaggressions include teachers being surprised by certain students’ achievements or holding tests on religious holidays, and peers imitating foreign accents or saying, “That’s so gay,” or “She’s so bipolar.”

Starting Important Conversations, and Keeping Them Going

Once during a faculty meeting, I witnessed an educator tell a white male colleague that he’d committed a microaggression. At the time, I didn’t know precisely what that meant. Nobody talked for a few uncomfortable seconds until someone changed the topic. Calling the man out in the moment was justified. After all, it’s everybody’s job to make diversity-sensitive norms explicit. But that moment was also a dialogue killer. Had there been previous conversations among the entire faculty about microaggressions, perhaps the entire incident could have been avoided.

Thoughtful conversations are also halted by whataboutism (“Why do they get to use racist words and we don’t?”), name-calling (“snowflakes,” “thought police”), and the unfortunate formula “strategic denial plus conjunction plus racist comment” (“I’m not racist, but...”).

How do you have a meaningful classroom dialogue about microaggressions? The trick is to plan a conversation on this topic before microaggressions ignite tensions. Set discussion ground rules , like “commit to learning, not debating,” and then show examples of microaggressions as a prelude to discussing why they’re hurtful.

If these types of conversations feel too challenging to you, contact a nearby university’s office of diversity and inclusion and invite someone with expertise in sensitive topics to address the class. They’ll model how to handle this discussion, so you can take the lead next time.

Resources to Counteract Implicit Bias and Microaggressions

There are a number of resources that can help K–12 faculty and adolescent learners counteract implicit bias and avoid the perpetuation of microaggressions.

Videos: Watch and discuss Dr. Yolanda Flores Niemann’s “ Microaggressions in the Classroom ” and Ahsante the Artist’s “ I, Too, Am Harvard ” during the next faculty meeting.

Checklists: Read “ Microaggressions in the Classroom ,” developed by the University of Denver, as well as Kevin Nadal’s list of microaggressions that harm LGBTQ people .

Activities: Sign up for a seven-day bias cleanse that emails daily tasks to reorient your thoughts on race, gender, and anti-LGBTQIA bias. And try Harvard’s Implicit Bias Test .

Readings: Check out multicultural texts suggested by the American Library Association .

Norms: Learn about culturally inclusive classrooms and establish classroom ground rules that promote inclusive language and behaviors.

Students and teachers are made of powerful feelings, but these feelings are not fixed and set in stone. Emotions can be identified, excavated, understood, and managed. And when we work through that process together, implicit bias can be unlearned .

17 Implicit Bias Examples

An implicit bias is an automatic and unconscious attitude that affects a person’s judgment, decision, or behavior. Because the bias operates on an unconscious level, it can have effects in which people are completely unaware.

Greenwald and Banaji (1995) are often credited with offering a formal definition of implicit bias:

“… introspectively unidentified (or inaccurately identified) traces of past experience that mediate favorable or unfavorable feeling, thought, or action toward social objects” (p. 8).

A great deal of research on implicit bias has revealed numerous manifestations. For example, implicit bias has been shown to operate in the criminal justice system, workplace, school setting and healthcare system.

Implicit biases can have their effect even though the results may be counter to the person’s objectives or declared beliefs.

Although most people think of themselves as rational and logical human beings, it turns out that much of our thinking processes are flawed and can be swayed by factors that are outside our conscious awareness.

Implicit Bias Examples

1. affinity bias.

People have a tendency to like people that are similar to themselves. When we meet someone that shares our values and beliefs, we feel a natural attraction to them. This often leads to people socializing and joining social groups of like-minded individuals.

Unfortunately, although this seems perfectly harmless, it does lead to several negative outcomes.

For example, when holding strong political views, people will form bonds with others that share those views. This limits the opportunities to understand alternative perspectives and can result in people becoming “set in their ways”.

Ideally, political discourse will occur among those with opposing views. This can facilitate mutual understanding and respect. That understanding and respect could lead to progress on controversial issues that would benefit society. However, the affinity bias makes this far less likely to occur.

Read Next: Overcome Implicit Biases with the Cultural Humility Approach

2. Beauty Bias

Beauty bias refers to the way people are perceived based on their high level of physical attractiveness. Conventional wisdom holds that the more attractive a person is, the easier they have it in life.

People often believe that being attractive makes it easier to land a good job, climb the corporate ladder, and just more likely to be successful in life in general.

That may be true in some circumstances, but in others, it most definitely is not. For example, when a highly attractive female applies for a job that is physically demanding, they are likely to be judged as incapable.

In other circumstances, being attractive in the workplace may create jealousy in colleagues and lead to social isolation or the sabotaging of promotional opportunities.

Being beautiful is a double-edged sword.

Ageism refers to discrimination against an individual strictly based on their age. Although the term is usually used in the context of being directed toward older individuals, younger generations can also be subjected to ageism.

Ageism can appear in many forms and affect many aspects of daily life; some ramifications can be quite severe. For example, ageism can affect employment and promotional opportunities as well as treatment in the healthcare system.

According to the World Health Organization , ageism is associated with earlier death, risky health behaviors, social isolation, loneliness, and depression.

4. Affect Heuristic

The affect heuristic occurs when a person makes a judgment based on their current emotional state.

Instead of conducting a thorough analysis of the pros and cons of a situation, the final decision is heavily influenced by emotions. In this way, the affect heuristic is a type of mental shortcut.

We can see examples of the affect heuristic in everyday life. For example, when being in a good mood, a person is open to suggestions and more likely to agree to the requests of others.

If a friend or colleague suggests a particular restaurant for lunch, the response may not be so much guided by an objective analysis of the type of food served or price point, but instead is influenced by a general state of positivity.

Likewise, when in a bad mood, people become a lot less agreeable. Suggestions are rejected outright and immediately. No need for analysis or deliberation, the answer is just “no”.

5. Anchoring Bias

Anchoring bias occurs when a person’s judgment is unduly influenced by an initial piece of information that serves as a reference point for the subsequent opinion.

This can lead to skewed judgments that are not based on a completely objective analysis.

Tversky and Kahneman (1974) identified the anchoring bias and provided a credible explanation:

“In many situations, people make estimates by starting from an initial value that is adjusted to yield the final answer. The initial value, or starting point, may be suggested by the formulation of the problem, or it may be the result of a partial computation … different starting points yield different estimates, which are biased toward the initial values. We call this phenomenon anchoring” (p. 1128).

For example, a car salesperson may start with a price point that is quite high. Then, when the price is lowered, a customer may think they are getting a bargain.

In reality, however, the price is still high. The salesperson has just used a high anchor to manipulate the customer’s perception of “value”.

6. Authority Bias

Authority bias is the tendency to believe, support, and obey people in positions of authority.

We are more likely to believe their judgments are correct, even if we may initially disagree.

The most famous psychological study on obedience to authority was conducted by Stanley Milgram (1963) in the 1960s and 70s. In this study, participants were told they were part of a study investigating the effects of electric shock on learning.

Participants were told that each time the person in the other room gave a wrong answer to a test question, they should be given an electric shock.

The results revealed that 65 percent of participants administered increasingly higher levels of shock, to the highest level, which was marked as “lethal,” despite the cries and screams heard in the other room.

The shocks were not actually delivered, and the person in the other room was an actor, but the findings of this study are astonishing.

They demonstrate the power of authority figures and the tendency for people to follow directions, seemingly no matter the consequences.

7. Confirmation Bias

Confirmation bias is one of the most prevalent biases of all. It refers to the tendency of people to look for information that confirms their opinion or beliefs.

For example, when deciding upon which news channel to watch, most people will choose the one that is consistent with their political ideology.

Other examples include following people on social media that agree with our views or reading articles with headlines that match our perceptions of the world. 

One of the many downsides of confirmation bias is that a person closes themselves off to encountering information that is more balanced. This means that instead of continuously evolving over a lifespan, nothing changes internally.

Although having our opinions confirmed on a regular basis can be good for one’s self-esteem, it also impedes personal growth .

8. Conformity Bias

People are indeed social animals. In nearly every culture throughout history, humans are known to form groups with each other, both large and small.

Of course, there are many survival benefits to existing in groups, such as sharing resources and protection from predators.

Unfortunately, conformity bias can also lead to shockingly horrid behavior. The tendency to go along with others can lead to what is sometimes called the “crowd mentality”.

Instead of using personal judgment and internal moral standards, being surrounded by others can overwhelm a person’s judgment.

Examples include succumbing to peer pressure to engage in risky behavior or participating in brawls even though a person has no vested interest in the situation.

Resisting the pressure to conform takes a strong will and may lead to somewhat negative consequences. Conformity bias may be one of the most dangerous biases that people possess.

9. Halo Effect

The halo effect occurs when we have a positive impression of someone based on our perception of them in a specific domain.

That impression then carries over to other domains. If they are good at A, then they must also be good at B, C, and D.

Psychologist Edward Thorndike (1920) first discovered the halo effect in one of his earlier studies, where supervisors’ ratings of their employees “were apparently affected by a marked tendency to think of the person in general as rather good or rather inferior and to color the judgments of the qualities by this general feeling” (p. 25).

The halo effect can be seen in marketing and advertising campaign strategies, consumer behavior, the classroom, and just about any event that involves people rating the performance of others.

10. Horns Effect

The horns effect is a bias that is similar to the halo effect, except instead of creating a positive impression of another person, the impression is negative.

So, if we first learn of something negative about an individual, that will affect our impression of them in other ways. We will rate their abilities and traits negatively, even if we have no information to support those conclusions.

The best demonstration of this phenomenon comes from a study by Nisbett and Wilson (1977). Participants watched a video that portrayed the same instructor as either likable or cold. Afterward, they rated the instructor on a variety of dimensions. The results were quite pronounced:

“A substantial majority of the subjects who saw the teacher in his warm guise rated his physical appearance as appealing, whereas a substantial majority of those who saw him in his cold guise rated his appearance as irritating. A majority of warm condition subjects rated the teacher’s mannerisms as appealing, whereas a majority of cold condition subjects rated bis mannerisms as irritating” (p. 253).

11. Hindsight Bias

Hindsight bias refers to the tendency of people to overestimate their ability to predict what will happen in the future.

 For example, when an unusual event occurs, some people will say that they “knew all along”. Even when not making any prior prediction regarding that event, the statement will still be made.

This is because human beings have a need to believe that their world is stable and predictable. When events occur that are counter to that presumption, it shakes our sense of stability and usually results in some kind of cognitive distortion to restore order.

That distortion often comes in the form of believing that we foresaw the event before it occurred.

In other circumstances, it can serve as an impression management strategy . Because a person does not want to look bad, they may say they anticipated a certain outcome in order to protect their self-esteem.

12. Overconfidence Bias

The overconfidence bias occurs when a person overestimates their intellect or abilities.

A person may believe they are capable of performing tasks that they are not actually capable of accomplishing.

Examples of the overconfidence bias can be seen in many aspects of daily life.

• The student who fails to study enough for an upcoming exam because they believe they are ready but ends up earning a D.
• The athlete who always wants to take the last shot but usually misses.
• The novice stock trader who thinks they can do better than professionals that have been in the business for decades.

In some circumstances, overestimating one’s abilities can have severe negative consequences.

13. Perception Bias

Perception bias is a generic term that refers to the mental shortcuts that people use to make sense of the world.

Perception bias includes lots of other biases that involve filtering information based on a preconceived notion, emotional state, or existing perspective.

Because people are simply bombarded with massive amounts of information, it is simply not possible to engage in a thorough analysis of all that we encounter. This would take too much time and absorb too much of our cognitive capacity.

Therefore, it is beneficial to take shortcuts in our thinking processes. Unfortunately, these shortcuts can rely on stereotypes or misleading information which clouds our judgment.

14. Recency Bias

People will often rely on the most recent information they have to guide their opinion or judgment. This is called the recency bias.

Instead of considering all the data available when forming an opinion, most people will be heavily influenced on whatever they have encountered most recently.

For example, the closing arguments in a legal case will be the easiest to remember when jurors are in deliberations regarding the verdict.

News stories about traumatic events such as an airplane crash or shark attack will have a strong influence on people’s perception of risk that actually overestimates the likelihood of those events.

In social relations, sometimes the last words a person spoke to someone will be what they remember most. This can affect their impression of that person and either be positive or negative, depending on what those last words were about.

15. Status Quo Bias

The status quo bias is a preference for keeping things the way they are. In a sense, it is more than just a cognitive bias because it can also be grounded in an emotional reaction to change.

Change can sometimes be scary. It contains many unknowns, while the situation we are in is familiar and may seem safer than going through change. So, the status quo bias is just as much an emotional reaction as it is a cognitive shortcut.

The status quo bias can explain why a person is sometimes very reluctant to leave an unhappy relationship. Terminating a relationship means going back into the dating pool, which is often full of disappointment.

The status quo can also explain a person’s hesitancy to leave their job and pursue their dreams. Having a stable income feels safe and provides a sense of security. This can make it difficult to leave one’s comfort zone to pursue an endeavor that may result in absolute failure.

16. Cultural Bias

Cultural bias happens when we process what we learn about another culture based on our own culture’s values and beliefs.

This is also sometimes referred to as ethnocentrism.

Instead of accepting and tolerating the ways of another culture, it can be easy to judge another culture through our existing worldview.

This makes it difficult to understand the perspective of people from foreign countries or those that have different religious beliefs.

For example, people from Western countries have strong beliefs about equality in the workplace, while other cultures may be more accepting of authoritarian leadership styles.

Table etiquette is another example of when cultural bias can occur. After being raised to share food from all the same bowls placed in the center of the table, it may seem selfish to see that people in a Western culture prefer to each have their own plate.  

17. Belief Bias

Belief bias occurs when a person believes in the logic of an argument based upon whether it supports their expected conclusions.

For example, if you really think that unicorns exist, you’ll be more inclined to believe in the logic of an argument presented to you if it concludes that unicorns are true.

By contrast, if you believe they aren’t real, but you’re presented with evidence that they are, then you’ll implicitly question the validity of the argument that’s been presented.

A belief bias helps us to make quick judgements about an argument, but it can also lead us astray when we’re presented with new, logical, information, because we will implicitly find it unlikely and therefore dismiss it without fully engaging with the logic behind it.

Implicit biases can occur in a surprisingly wide range of situations. Because an implicit bias operates at a level beyond conscious awareness, it can affect our judgments automatically.

These biases come in many forms. For example, when we form a general impression of someone, that will cloud our judgment of them in other ways, either positively (halo effect) or negatively (horns effect).

Our opinions of the elderly (ageism) or the abnormally attractive (beauty bias), can lead to negative assumptions about their abilities or level of intelligence.

Moreover, when traveling to a foreign country, it may be difficult to accept customs that are so disparate from our own (cultural bias) and make us more likely to seek out and socialize with others that share our ways (affinity bias).

As long as people will need to think, there will be biases, both good and bad. It’s a part of human nature that is difficult to overcome.

Dardenne, B., & Leyens, J. (1995). Confirmation bias as a social skill. Personality and Social Psychology Bulletin, 21 (11), 1229–1239.

Delagrave, AM. (2011). The beauty bias: The injustice of appearance in life and law (review). Canadian Journal of Women and the Law, 23 . https://doi.org/10.3138/cjwl.23.1.359

Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102 (1), 4–27. https://doi.org/10.1037/0033-295X.102.1.4

Johnson, S. K., Podratz, K. E., Dipboye, R. L., & Gibbons, E. (2010). Physical attractiveness biases in ratings of employment suitability: Tracking down the “beauty is beastly” effect. The Journal of Social Psychology , 150 (3), 301–318. https://doi.org/10.1080/00224540903365414

Johnson, B. D., & King, R. D. (2017). Facial profiling: Race, physical appearance, and punishment. Criminology , 55 (3), 520-547.

Milgram, S. (1963). Behavioral study of obedience. Journal of Abnormal and Social Psychology,

67 , 371–378.

Narayan M. C. (2019). CE: Addressing implicit bias in nursing: A review. The American Journal of Nursing , 119 (7), 36–43. https://doi.org/10.1097/01.NAJ.0000569340.27659.5a

Nisbett, R.E., & Wilson, T.D. (1977). The halo effect: Evidence for unconscious alteration of judgments. Journal of Personality and Social Psychology, 35 , 250-256.

Oberai, H., & Anand, I. M. (2018). Unconscious bias: Thinking without thinking. Human Resource Management International Digest . https://doi.org/10.1108/HRMID-05-2018-0102

Thorndike, E.L. (1920).  A constant error in psychological ratings . Journal of Applied Psychology, 4 , 25-29. 

Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty.  Science ,  185 (4157), 1124–1131.

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Positive Punishment Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Dissociation Examples (Psychology)
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Zone of Proximal Development Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ Perception Checking: 15 Examples and Definition

Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

• Chris Drew (PhD) #molongui-disabled-link 25 Positive Punishment Examples
• Chris Drew (PhD) #molongui-disabled-link 25 Dissociation Examples (Psychology)
• Chris Drew (PhD) #molongui-disabled-link 15 Zone of Proximal Development Examples
• Chris Drew (PhD) #molongui-disabled-link Perception Checking: 15 Examples and Definition

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

Implicit Bias in the Workplace Essay

In the context of today’s rapidly changing world, the notion of discrimination has become unacceptable in any of its manifestations. Speaking of the legislative level of the issue, authorities from all over the world have made great progress in terms of bias prevention in the workplace, medical care, and basic social needs. However, the one full-scale problem tackling our society today is a demonstration of implicit bias in the workplace. The very notion of implicit bias presupposes that people experience stereotypical attitudes towards individuals of specific race, ethnicity, gender, or sexual affiliation without conscious perception of such discrimination. As a result, people who do their best at combating prejudice, still demonstrate discrimination against their colleagues. In today’s overall perception of equality in the workplace, the notions of gender and race are the ones that matter and stand out the most. However, I would like to dedicate more attention to the issue of racial bias, as I feel that the problem still has a lot to consider to eradicate them from the common consciousness.

The modern version of the workplace depiction has overcome many challenges to be in the place it is today, giving people the opportunity to find a job regardless of gender, race, and ethnicity. However, once people get certain positions within a workplace, it becomes clear that there is a kind of major discrepancy between the positions taken by white people and employees of a different race (Thomas, 2019). When speaking of efforts to achieve racially just workplace environments, workers struggle with implicit racism in several manifestations. First of all, African-American employees have to deal with aversive racism, which makes people change their behavioral patterns around them (Roberts & Mayo, n.d.). Another implicit racism expression is related to people’s expectations towards the occupations common for African-Americans, assuming that they do not frequently hold a high position within the enterprise. Finally, implicit racism in the workplace is affected by people who ignore the fact black people still face discrimination by reassuring that the modern labor market is open for any race or ethnicity on equal terms.

In fact, over the last years, considerably more African-Americans hold leading positions, creating an impression of racism eradication in the unit. However, besides all the workload, they are to additionally face mistreatment or tension coming from the employees, limiting one’s ability to become an efficient leader (Caver & Livers, 2002). Such an attitude is also known under the term “miasma,” causing high-stress rates among African-American employees across the state.

Bearing this information in mind, I tried to define which implicit bias in the workplace still interfere with my perception of racial equality. As a result, I understood that my relationship with African-American employees frequently includes minor manifestations of aversive racism, as I subconsciously pay too much attention to the way I act around them. As a result, while trying too hard to avoid potentially awkward situations, African-Americans might think of such an attitude as an implicit offense. Moreover, on the level of my subconscious, I often misinterpret the very notion of equality. Hence, instead of showing racial diversity recognition and respect, I sometimes try to diminish the cultural difference between races to perceive it as “equal.” I do understand the cultural affiliation and historical difference between us, which will never allow us to explicitly understand everything people have to go through daily. However, this understanding sometimes looks like it has been significantly undermined under the social pressure of constant attempts to equal the racial experiences.

Caver, K. A., & Livers, A. B. (2002). Dear white boss . Web.

Roberts, L. M., & Mayo, A. J. (n.d.). Towards a racially just workplace. Web.

Thomas, C. (2019). Is empathy the link? An exploration of implicit racial bias in the workplace (Doctoral dissertation, University of Pennsylvania).

• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2022, February 8). Implicit Bias in the Workplace. https://ivypanda.com/essays/implicit-bias-in-the-workplace/

"Implicit Bias in the Workplace." IvyPanda , 8 Feb. 2022, ivypanda.com/essays/implicit-bias-in-the-workplace/.

IvyPanda . (2022) 'Implicit Bias in the Workplace'. 8 February.

IvyPanda . 2022. "Implicit Bias in the Workplace." February 8, 2022. https://ivypanda.com/essays/implicit-bias-in-the-workplace/.

1. IvyPanda . "Implicit Bias in the Workplace." February 8, 2022. https://ivypanda.com/essays/implicit-bias-in-the-workplace/.

Bibliography

IvyPanda . "Implicit Bias in the Workplace." February 8, 2022. https://ivypanda.com/essays/implicit-bias-in-the-workplace/.

• The Fiction Character`s PTSD Diagnosis: Rambo
• The Impact of Financial Circumstances on Student Health
• Cognitive Dissonance and Self-Perception Theories
• Sarbanes-Oxley Act: The End of the Unethical Behavior
• Basic Thoughts on How to Make a Company Successful
• Ethics in a Consumer Society: Bauman's Arguments
• Negligence in Businesses: Heading off the Liability Headache
• Business Ethics and Ethical Decision Making

Taking steps to recognize and correct unconscious assumptions toward groups can promote health equity.

JENNIFER EDGOOSE, MD, MPH, MICHELLE QUIOGUE, MD, FAAFP, AND KARTIK SIDHAR, MD

Fam Pract Manag. 2019;26(4):29-33

Author disclosures: no relevant financial affiliations disclosed.

Jamie is a 38-year-old woman and the attending physician on a busy inpatient teaching service. On rounds, she notices several patients tending to look at the male medical student when asking a question and seeming to disregard her. Alex is a 55-year-old black man who has a history of diabetic polyneuropathy with significant neuropathic pain. His last A1C was 7.8. He reports worsening lower extremity pain and is frustrated that, despite his bringing this up repeatedly to different clinicians, no one has addressed it. Alex has been on gabapentin 100 mg before bed for 18 months without change, and his physicians haven't increased or changed his medication to help with pain relief.

Alisha is a 27-year-old Asian family medicine resident who overhears labor and delivery nurses and the attending complain that Indian women are resistant to cervical exams.

These scenarios reflect the unconscious assumptions that pervade our everyday lives, not only as practicing clinicians but also as private citizens. Some of Jamie's patients assume the male member of the team is the attending physician. Alex's physicians perceive him to be a “drug-seeking” patient and miss opportunities to improve his care. Alisha is exposed to stereotypes about a particular ethnic group.

Although assumptions like these may not be directly ill-intentioned, they can have serious consequences. In medical practice, these unconscious beliefs and stereotypes influence medical decision-making. In the classic Institute of Medicine report “Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care,” the authors concluded that “bias, stereotyping, and clinical uncertainty on the part of health care providers may contribute to racial and ethnic disparities in health care” often despite providers' best intentions. 1 For example, studies show that discrimination and bias at both the individual and institutional levels contribute to shocking disparities for African-American patients in terms of receiving certain procedures less often or experiencing much higher infant mortality rates when compared with non-Hispanic whites. 2 , 3 As racial and ethnic diversity increases across our nation, it is imperative that we as physicians intentionally confront and find ways to mitigate our biases.

Implicit bias is the unconscious collection of stereotypes and attitudes that we develop toward certain groups of people, which can affect our patient relationships and care decisions.

You can overcome implicit bias by first discovering your blind spots and then actively working to dismiss stereotypes and attitudes that affect your interactions.

While individual action is helpful, organizations and institutions must also work to eliminate systemic problems.

DEFINING AND REDUCING IMPLICIT BIAS

For the last 30 years, science has demonstrated that automatic cognitive processes shape human behavior, beliefs, and attitudes. Implicit or unconscious bias derives from our ability to rapidly find patterns in small bits of information. Some of these patterns emerge from positive or negative attitudes and stereotypes that we develop about certain groups of people and form outside our own consciousness from a very young age. Although such cognitive processes help us efficiently sort and filter our perceptions, these reflexive biases also promote inconsistent decision making and, at worst, systematic errors in judgment.

Cognitive processes lead us to associate unconscious attributes with social identities. The literature explores how this influences our views on race, ethnicity, age, gender, sexual orientation, and weight, and studies show many people are biased in favor of people who are white, young, male, heterosexual, and thin. 4 Unconsciously, we not only learn to associate certain attributes with certain social groupings (e.g., men with strength, women with nurturing) but also develop preferential ranking of such groups (e.g., preference for whites over blacks). This unconscious grouping and ranking takes root early in development and is shaped by many outside factors such as media messages, institutional policies, and family beliefs. Studies show that health care professionals have the same level of implicit bias as the general population and that higher levels are associated with lower quality care. 5 Providers with higher levels of bias are more likely to demonstrate unequal treatment recommendations, disparities in pain management, and even lack of empathy toward minority patients. 6 In addition, stressful, time-pressured, and overloaded clinical practices can actually exacerbate unconscious negative attitudes. Although the potential impact of our biases can feel overwhelming, research demonstrates that these biases are malleable and can be overcome by conscious mitigation strategies. 7

We recommend three overarching strategies to mitigate implicit bias – educate, expose, and approach – which we will discuss in greater detail. We have further broken down these strategies into eight evidence-based tactics you can incorporate into any quality improvement project, diagnostic dilemma, or new patient encounter. Together, these eight tactics spell out the mnemonic IMPLICIT. (See “ Strategies to combat our implicit biases .”)

When we fail to learn about our blind spots, we miss opportunities to avoid harm. Educating ourselves about the reflexive cognitive processes that unconsciously affect our clinical decisions is the first step. The following tactics can help:

Introspection . It is not enough to just acknowledge that implicit bias exists. As clinicians, we must directly confront and explore our own personal implicit biases. As the writer Anais Nin is often credited with saying, “We don't see things as they are, we see them as we are.” To shed light on your potential blind spots and unconscious “sorting protocols,” we encourage you to take one or more implicit association tests . Discovering a moderate to strong bias in favor of or against certain social identities can help you begin this critical step in self exploration and understanding. 8 You can also complete this activity with your clinic staff and fellow physicians to uncover implicit biases as a group and set the stage for addressing them. For instance, many of us may be surprised to learn after taking an implicit association test that we follow the typical bias of associating males with science — an awareness that may explain why the patient in our first case example addressed questions to the male medical student instead of the female attending.

Mindfulness .It should come as no surprise that we are more likely to use cognitive shortcuts inappropriately when we are under pressure. Evidence suggests that increasing mindfulness improves our coping ability and modifies biological reactions that influence attention, emotional regulation, and habit formation. 9 There are many ways to increase mindfulness, including meditation, yoga, or listening to inspirational texts. In one study, individuals who listened to a 10-minute meditative audiotape that focused them and made them more aware of their sensations and thoughts in a nonjudgmental way caused them to rely less on instinct and show less implicit bias against black people and the aged. 10

It is also helpful to expose ourselves to counter-stereotypes and to focus on the unique individuals we interact with. Similarity bias is the tendency to favor ourselves and those like us. When our brains label someone as being within our same group, we empathize better and use our actions, words, and body language to signal this relatedness. Experience bias can lead us to overestimate how much others see things the same way we do, to believe that we are less vulnerable to bias than others, and to assume that our intentions are clear and obvious to others. Gaining exposure to other groups and ways of thinking can mitigate both of these types of bias. The following tactics can help:

Perspective-taking . This tactic involves taking the first-person perspective of a member of a stereotyped group, which can increase psychological closeness to that group. 8 Reading novels, watching documentaries, and listening to podcasts are accessible ways to reach beyond our comfort zone. To authentically perceive another person's perspective, however, you should engage in positive interactions with stereotyped group members in real life. Increased face-to-face contact with people who seem different from you on the surface undermines implicit bias.

Learn to slow down . To recognize our reflexive biases, we must pause and think. For example, the next time you interact with someone in a stereotyped group or observe societal stereotyping, such as through the media, recognize what responses are based on stereotypes, label those responses as stereotypical, and reflect on why the responses occurred. You might then consider how the biased response could be avoided in the future and replace it with an unbiased response. The physician treating Alex in the introduction could use this technique by slowing down and reassessing his medical care. By acknowledging the potential for bias, the physician may recognize that safe options remain for managing Alex's neuropathic pain.

Additionally, research strongly supports the use of counter-stereotypic imaging to replace automatic responses. 11 For example, when seeking to contradict a prevailing stereotype, substitute highly defined images, which can be abstract (e.g., modern Native Americans), famous (e.g., minority celebrities like Oprah Winfrey or Lin-Manuel Miranda), or personal (e.g., your child's teacher). As positive exemplars become more salient in your mind, they become cognitively accessible and challenge your stereotypic biases.

Individuation . This tactic relies on gathering specific information about the person interacting with you to prevent group-based stereotypic inferences. Family physicians are trained to build and maintain relationships with each individual patient under their care. Our own social identities intersect with multiple social groupings, for example, related to sexual orientation, ethnicity, and gender. Within these multiplicities, we can find shared identities that bring us closer to people, including shared experiences (e.g., parenting), common interests (e.g., sports teams), or mutual purpose (e.g., surviving cancer). Individuation could have helped the health care workers in Alisha's labor and delivery unit to avoid making judgments based on stereotypes. We can use this tactic to help inform clinical decisions by using what we know about a person's specific, individual, and unique attributes. 11

Like any habit, it is difficult to change biased behaviors with a “one shot” educational approach or awareness campaign. Taking a systematic approach at both the individual and institutional levels, and incorporating a continuous process of improvement, practice, and reflection, is critical to improving health equity.

Check your messaging . Using very specific messages designed to create a more inclusive environment and mitigate implicit bias can make a real difference. As opposed to claiming “we don't see color” or using other colorblind messaging, statements that welcome and embrace multiculturalism can have more success at decreasing racial bias.

Institutionalize fairness . Organizations have a responsibility to support a culture of diversity and inclusion because individual action is not enough to deconstruct systemic inequities. To overcome implicit bias throughout an organization, consider implementing an equity lens – a checklist that helps you consider your blind spots and biases and assures that great ideas and interventions are not only effective but also equitable (an example is included in the table above ). Another example would be to find opportunities to display images in your clinic's waiting room that counter stereotypes. You could also survey your institution to make sure it is embracing multicultural (and not colorblind) messaging.

Take two . Resisting implicit bias is lifelong work. The strategies introduced here require constant revision and reflection as you work toward cultural humility. Examining your own assumptions is just a starting point. Talking about implicit bias can trigger conflict, doubt, fear, and defensiveness. It can feel threatening to acknowledge that you participate in and benefit from systems that work better for some than others. This kind of work can mean taking a close look at the relationships you have and the institutions of which you are a part.

MOVING FORWARD

Education, exposure, and a systematic approach to understanding implicit bias may bring us closer to our aspirational goal to care for all our patients in the best possible way and move us toward a path of achieving health equity throughout the communities we serve. The mnemonic IMPLICIT can help us to remember the eight tactics we all need to practice. While disparities in social determinants of health are often beyond the control of an individual physician, we can still lead the fight for health equity for our own patients, both from within and outside the walls of health care. With our specialty-defining goal of getting to know each patient as a unique individual in the context of his or her community, family physicians are well suited to lead inclusively by being humble, respecting the dignity of each person, and expressing appreciation for how hard everyone works to overcome bias.

Smedley BD, Stith AY, Nelson AR, eds Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care . Washington, DC: Institute of Medicine, National Academy Press; 2003.

Hannan EL, van Ryn M, Burke J, et al.; Access to coronary artery bypass surgery by race/ethnicity and gender among patients who are appropriate for surgery. Med Care . 1999;37(1):68-77.

Infant mortality and African Americans. U.S Department of Health and Human Services Office of Minority Health website. https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=23 . Updated Nov. 9, 2017. Accessed June 10, 2019.

Nosek BA, Smyth FL, Hansen JJ, et al.; Pervasiveness and correlates of implicit attitudes and stereotypes. Eur Rev Soc Psychol . 2007;18(1):36-88.

FitzGerald C, Hurst S. Implicit bias in healthcare professionals: a systematic review. BMC Med Ethics . 2017;18(1):19.

Maina IW, Belton TD, Ginzberg S, Singh A, Johnson TJ. A decade of studying implicit racial/ethnic bias in healthcare providers using the implicit association test. Soc Sci Med . 2018;199:219-229.

Charlesworth TES, Banaji MR. Patterns of implicit and explicit attitudes: I. long-term change and stability from 2007 to 2016. Psychol Sci . 2019;30(2):174-192.

Sukhera J, Wodzinski M, Teunissen PW, Lingard L, Watling C. Striving while accepting: exploring the relationship between identity and implicit bias recognition and management. Acad Med . 2018;93(11S Association of American Medical Colleges Learn Serve Lead: Proceedings of the 57th Annual Research in Medical Education Sessions):S82-S88.

Burgess DJ, Beach MC, Saha S. Mindfulness practice: A promising approach to reducing the effects of clinician implicit bias on patients. Patient Educ Couns . 2017;100(2):372-376.

Lueke A, Gibson B. Mindfulness meditation reduces implicit age and race bias: the role of reduced automaticity of responding. Soc Psychol Personal Sci . 2015;6(3):284-291.

Devine PG, Forscher PS, Austin AJ, Cox WTL. Long-term reduction in implicit race bias: a prejudice habit-breaking intervention. J Exp Soc Psychol . 2012;48(6):1267-1278.

Continue Reading

More in FPM

More in pubmed.

Copyright © 2019 by the American Academy of Family Physicians.

This content is owned by the AAFP. A person viewing it online may make one printout of the material and may use that printout only for his or her personal, non-commercial reference. This material may not otherwise be downloaded, copied, printed, stored, transmitted or reproduced in any medium, whether now known or later invented, except as authorized in writing by the AAFP.  See permissions  for copyright questions and/or permission requests.

Copyright © 2024 American Academy of Family Physicians. All Rights Reserved.

Home — Essay Samples — Psychology — Bias — Implicit Bias: Unraveling Its Impact and Solutions

Implicit Bias: Unraveling Its Impact and Solutions

• Categories: Bias Social Norms

About this sample

Words: 945 |

Published: Sep 12, 2023

Words: 945 | Pages: 2 | 5 min read

Table of contents

The essence of implicit bias, the genesis of implicit bias, the consequences of implicit bias, addressing implicit bias, awareness-raising, training and education, policy change.

Cite this Essay

Let us write you an essay from scratch

• 450+ experts on 30 subjects ready to help
• Custom essay delivered in as few as 3 hours

Get high-quality help

Verified writer

• Expert in: Psychology Sociology

+ 120 experts online

By clicking “Check Writers’ Offers”, you agree to our terms of service and privacy policy . We’ll occasionally send you promo and account related email

No need to pay just yet!

Related Essays

1 pages / 559 words

1 pages / 386 words

4 pages / 1906 words

3 pages / 1544 words

Remember! This is just a sample.

You can get your custom paper by one of our expert writers.

121 writers online

Still can’t find what you need?

Browse our vast selection of original essay samples, each expertly formatted and styled

Related Essays on Bias

A cognitive bias disrupts a normal cognitive process like reasoning, evaluating, and recalling memories. This perception mainly results from the tendency for individuals to put their own beliefs, mood, and preferences above any [...]

Discrimination, its very existence can be considered one of civilized society’s ugliest and permanent scars. Like most other sins of its kind, rooting back to the potential moral corruption of the person, discrimination has no [...]

Of course, that is oversimplified do people believe in that the past is real, arguments can inform whether the history existed but will leave that unparalleled. The thing is that there is not any record, anywhere, including the [...]

A confirmation bias is an individual’s tendency to filter out information that aligns with their own existing beliefs and ideas. It is a trap that most of us fall into unknowingly. Even when evidence that contradicts our views [...]

All four articles share the same main theme, artificial intelligence: on people, the government, and the workforce. An article on Forbes magazine revolves around the importance of trust between AI and the consumer, and how trust [...]

Research into the concept and assessment of intelligence, clearly indicates that no universal consensus exists. On the contrary, many theories and much research have emerged with understanding continuing to be investigated and [...]

Related Topics

By clicking “Send”, you agree to our Terms of service and Privacy statement . We will occasionally send you account related emails.

Where do you want us to send this sample?

By clicking “Continue”, you agree to our terms of service and privacy policy.

Be careful. This essay is not unique

This essay was donated by a student and is likely to have been used and submitted before

Download this Sample

Free samples may contain mistakes and not unique parts

Sorry, we could not paraphrase this essay. Our professional writers can rewrite it and get you a unique paper.

Please check your inbox.

We can write you a custom essay that will follow your exact instructions and meet the deadlines. Let's fix your grades together!

Get Your Personalized Essay in 3 Hours or Less!

We use cookies to personalyze your web-site experience. By continuing we’ll assume you board with our cookie policy .

• Instructions Followed To The Letter
• Deadlines Met At Every Stage
• Unique And Plagiarism Free

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Research bias
• What Is Implicit Bias? | Definition & Examples

What Is Implicit Bias? | Definition & Examples

Published on 27 January 2023 by Kassiani Nikolopoulou .

Implicit bias is a collection of associations and reactions that emerge automatically upon encountering an individual or group. We associate negative or positive stereotypes with certain groups and let these influence how we treat them rather than remaining neutral.

This can lead to discriminatory behavior in a wide range of contexts such as healthcare, the workplace, and education.

Table of contents

What is implicit bias, what causes implicit bias, implicit vs. explicit bias, implicit bias examples, what is the harvard implicit bias association test (iat), how to reduce implicit bias, other types of research bias, frequently asked questions about implicit bias.

Implicit bias is an unconscious preference for (or aversion to) a particular person or group. Although these feelings can be either positive or negative, they cause us to be unfair towards others. Affinity bias or the tendency to favor people who are similar to us, is an example of this unfair behavior. However, any aspect of an individual’s identity, such as age, gender, or socioeconomic background, can be the target of implicit bias.

Under implicit bias, we are unaware that our biases (rather than objective facts) affect our decisions and judgments. For example, when most people hear the word “kindergarten teacher,” they are more likely to picture a female. This can happen without intention or awareness and may even contradict openly held beliefs. Because implicit bias is unconscious, it is difficult to acknowledge and control.

Implicit bias occurs due to unconscious mental processes. There are several factors at play in the development of implicit biases:

• Our brains create categories . We have the natural tendency to assign everything we see into a category. Even though this happens unconsciously, after categorizing things or people, we also assign a positive or negative association to them. Categories allow our brains to know what to do or how to behave. The downside of this is that classifications often cause us to overgeneralize.
• We rely on mental shortcuts. Most of the time, we rely on “automatic” information processing that involves little conscious thought. This allows us to exert little mental effort in our everyday lives and make swift judgments.
• Social and cultural influences. Our upbringing, social environment, and direct and indirect experiences with members of various social groups imprint on us. These shape our perception at a deeper level, even if we are not conscious of it.

Both implicit and explicit bias involve judging others based on our assumptions rather than the situation or the facts at hand. However, they are actually quite different.

• Implicit bias occurs when we have an inclination for or against a person or group that emerges automatically. In other words, our evaluation, positive or negative, is unintentional and beyond our conscious awareness.
• Conversely, explicit bias refers to positive or negative attitudes that we are fully aware of. We openly express them and share them with others, because these attitudes are part of our worldview.

Despite their differences, implicit bias can be just as problematic as explicit bias because both may lead to discriminatory behavior.

Implicit bias can lead to discriminatory behavior when it comes to hiring a diverse workforce.

In a field experiment measuring racial discrimination in the labor market, researchers responded to job ads in Boston and Chicago using fictitious resumes. To manipulate perception of race, each resume was assigned either an African-American-sounding name or a white-sounding name. The results showed significant discrimination against African-American names: applicants with white-sounding names received 50 percent more callbacks for interviews.

The amount of discrimination was uniform across occupations and industries. Additionally, federal contractors and employers who mentioned “Equal Opportunity Employer” in their ad discriminated as much as other employers.

The researchers concluded that there was little evidence that employers were trying to infer something other than race, such as social class, from the name.

The Harvard Implicit Bias Association Test (IAT) is a computer-based assessment measuring the strength of associations between concepts or stereotypes to reveal an individual’s implicit or subconscious biases.

The idea behind IAT is that, while we can measure explicit bias by asking respondents directly about their views regarding something like gender roles, the same does not apply for implicit biases. When we want to measure hidden or implicit attitudes, we need to do so indirectly. Otherwise, respondents will not answer truthfully due to social desirability or a lack of awareness of their own biases.

There are different versions of the IAT, but it typically consists of five rounds. In each round, respondents need to quickly sort words (e.g., “parents”) into categories that are on the left- and right-hand side of the screen (e.g., “career” and “family”). The key assumption underlying any IAT is that the stronger the association a respondent has between two concepts, the faster they are to make these associations.

Understanding implicit bias is critical because both positive and negative unconscious beliefs can lead to structural and systemic inequalities. However, because it operates outside our awareness, if we want to reduce it, we first need to become conscious of it. The following strategies can be helpful:

• Taking the IAT can help you realize that everyone, including you, has implicit biases. Recognizing them for what they are increases the likelihood that next time you won’t let these hidden biases affect your behavior.
• Positive intergroup contact. Unconscious bias towards a particular group can be reduced through interaction with members of that group. For example, you can make it a point to engage in activities that include individuals from diverse backgrounds.
• Counter-stereotyping. Exposure to information that defies stereotypes that persist about groups or individuals, such as images of female scientists, can counter gender stereotypes.
• Implicit bias training. Although raising awareness is important, it’s not enough. The most successful training programs are ones that allow individuals to discover their biases in a non-confrontational manner and also give them the tools to reduce and manage their biases.

Cognitive bias

• Confirmation bias
• Baader–Meinhof phenomenon
• Availability heuristic
• Halo effect
• Framing effect
• Affect heuristic
• Representativeness heuristic
• Anchoring heuristic
• Primacy bias
• Optimism bias

Selection bias

• Sampling bias
• Ascertainment bias
• Attrition bias
• Self-selection bias
• Survivorship bias
• Nonresponse bias
• Undercoverage bias
• Hawthorne effect
• Observer bias
• Omitted variable bias
• Publication bias
• Pygmalion effect
• Recall bias
• Social desirability bias
• Placebo effect
• Actor-observer bias
• Ceiling effect
• Ecological fallacy
• Affinity bias

Bias is a systematic error in the design, administration, or analysis of a study. Because of bias, study results deviate from their true value and researchers draw erroneous conclusions.

There are several types of bias and different research designs or fields are susceptible to different types of research bias. For example, in health research, bias arises from two main sources:

• The approach adopted for selecting study participants
• The approach adopted for collecting or measuring data

These are, respectively, selection bias and information bias .

Bias can be either positive or negative. However, all forms of bias (whether favourable or unfavourable) prevent us from judging others fairly.

For example, because of explicit bias , a teacher might openly claim that students from a certain ethnic background are exceptionally good in math. Even though this sounds positive, it means that other students are automatically treated as second-rate. For this reason, bias is linked to unfairness and thus has a negative connotation.

There are two main types of bias:

Implicit bias is the positive or negative attitudes, feelings, and stereotypes we maintain about members of a certain group without us being consciously aware of them.

Explicit bias is the positive or negative attitudes, feelings, and stereotypes we maintain about others while being consciously aware of them.

The opposite of implicit bias is explicit bias , or conscious bias. This refers to preferences, opinions, and attitudes of which people are generally consciously aware. In other words, explicit bias is expressed openly and deliberately.

Sources for this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

Nikolopoulou, K. (2023, January 27). What Is Implicit Bias? | Definition & Examples. Scribbr. Retrieved 20 May 2024, from https://www.scribbr.co.uk/bias-in-research/implicit-bias-meaning/

Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination. American Economic Review , 94 (4), 991–1013. https://doi.org/10.1257/0002828042002561

Daumeyer, N. M., Onyeador, I. N., Brown, X., & Richeson, J. A. (2019). Consequences of attributing discrimination to implicit vs. explicit bias. Journal of Experimental Social Psychology , 84 , 103812. https://doi.org/10.1016/j.jesp.2019.04.010

Is this article helpful?

Kassiani Nikolopoulou

Other students also liked, what is explicit bias | definition & examples, what is optimism bias | definition & examples, what is the halo effect | definition & examples.

• Bachelor’s Degrees
• Master’s Degrees
• Doctorate Degrees
• Certificate Programs
• Nursing Degrees
• Cybersecurity
• Human Services
• Science & Mathematics
• Communication
• Liberal Arts
• Social Sciences
• Computer Science
• Admissions Overview
• Tuition and Financial Aid
• Incoming Freshman and Graduate Students
• Transfer Students
• Military Students
• International Students
• Early Access Program
• About Maryville
• Our Faculty
• Our Approach
• Our History
• Accreditation
• Tales of the Brave
• Student Support Overview
• Online Learning Tools
• Infographics

Home / Blog

How to Identify and Overcome Your Implicit Bias

July 20, 2021 

We all have biases — unsupported assumptions we make about people or groups. Implicit bias, also commonly known as unconscious bias, refers to the various social stereotypes and judgments that people unknowingly assign to others based on a variety of factors, such as their age, socioeconomic status, weight, gender, race, or sexual orientation. And while these biases aren’t always negative, they’re shaped by a survival instinct that causes people to associate with people they perceive to be similar to them, because they’re deemed to be “safe.”

Examples of unconscious biases are present throughout our personal and professional lives. In Blink: The Power of Thinking Without Thinking, Malcolm Gladwell notes that in the general population, roughly 3.9% of adult men are 6 foot, 2 inches or taller. Yet among a random sampling of CEOs, he found that nearly a third, or roughly 33.3%, fell into this group.

According to Gladwell, this could be linked to an unconscious belief that height correlates with success. This hypothesis is further underscored by a 2020 Chinese study that found that each centimeter in height above average correlated with a 10% to 13% increase in annual earnings.

In an example from 2018, two Black men walked into a Philadelphia Starbucks to attend a business meeting. The manager asked them to leave, and they declined, saying they were waiting for their associate. The manager called the police, who then arrested the men. In interviews after the arrest, the men said they believed the manager had targeted them because of their race. Starbucks responded by holding companywide training to “address implicit bias, promote conscious inclusion, and prevent discrimination.”

Everyone holds implicit beliefs about various social groups, and these biases can have a negative impact in our social, study, and work environments. Implicit biases are harmful because they influence the way we perceive and interact with others — and can lead us to depersonalize people from different groups based on perceived characteristics. Learning to identify and overcome them is an important step toward overcoming prejudice and social and racial stereotypes.

What Is Implicit Bias?

Whereas explicit biases are those that people express openly (e.g., arguing that mothers of young children shouldn’t hold management positions), implicit biases often lie outside of our conscious awareness.

For example, if a manager assigns a tech-heavy task to a young employee instead of an older one based on the unspoken assumption that younger staff members are better with technology, implicit bias is at play. Unconscious bias can also occur in the classroom; for example, students may marginalize non-native English speakers when choosing work groups, with the unconscious assumption that they may not perform as well as native English-speaking peers.

The insidious nature of bias lies in its unconscious nature, as our implicit biases often contradict the values that we aspire to. And when people aren’t even aware that they’re doing something, it can be difficult to correct.

Types of Implicit Bias

The first step toward addressing implicit biases involves learning to recognize them. Among the various implicit biases prevalent throughout society are some such as race and ethnicity bias, age bias, gender bias, LGBTQIA+ community bias, and ability bias.

Race and Ethnicity Bias

Race and ethnicity bias occurs when people assume certain characteristics about someone based on their race or ethnicity, such as assuming that all Asian students are good at math or that all Hispanic individuals are English-language learners, and then take actions that reinforce those biases — unconsciously overlooking a Hispanic employee for a task that requires strong English communication skills, for example.

Age bias occurs when people make assumptions about others based on their age, such as when a hiring manager looking for a social media-savvy applicant rejects a resume because the graduation date tips off that the applicant is middle-aged, unconsciously assuming that the candidate wouldn’t be adept at social media management.

Gender Bias

Gender bias occurs when people assume one gender is better suited for a particular job — such as welding or babysitting — regardless of an applicant’s experience level.

LGBTQIA+ Community Bias

Assuming that lesbians can’t relate to men, and so reflexively declining to pair them with male teammates; assigning gay men to workplace tasks involving design without thinking of the reasons behind their choice; and unconsciously overlooking bisexuals for leadership positions based on an incorrect assumption that they “can’t make up their minds” are examples of LGBTQIA+ community bias. LGBTQIA+ community bias is also prevalent in the healthcare system. For example, when a nurse practitioner asks a female-presenting woman if she has a boyfriend when discussing her sexual history, implicit bias is at play.

Ability Bias

Ability bias is prevalent throughout society. Examples include hiring managers who are less likely to select a candidate with a disability because they unconsciously assume they’ll be more likely to take sick leave, and individuals who assume that all people who struggle with mental illness are prone to violent or dangerous behavior and so, without knowing they’re doing so, restrict them from certain roles.

Other Types of Bias

Implicit bias can take many other forms, such as:

• Affinity Bias: The tendency for individuals to gravitate toward people similar to themselves.
• Beauty Bias: The tendency for individuals to treat attractive people more favorably.
• Name Bias: The tendency for individuals to judge someone based on their name — and thus perceived background — which can negatively impact a company’s hiring processes.
• Weight Bias: The tendency for individuals to judge someone negatively, or assume negative things about them, if they’re overweight or underweight.

Overcoming Implicit Bias

People can use several different strategies to overcome and address implicit biases, although this is an area that no one can ever fully master. Examples include striving to identify and understand your implicit biases, proactively becoming more inclusive, and spending time with people who are different from you.

Identify and Evaluate Your Own Biases

The first step toward overcoming your implicit biases is to identify them. Reflect on your biases and be proactive in identifying the negative stereotypes you have about others. One way is to take one of Project Implicit’s Implicit Association Tests, which measure topics such as race, gender, weight, and religion.

The American Academy of Family Physicians (AAFP) discusses eight tactics that can be used to reduce implicit biases, using the acronym IMPLICIT:

• Introspection: Set aside time to understand your biases by taking a personal inventory of them. This can be done by taking tests to identify the biases you may have.
• Mindfulness: Once you understand the biases you hold, be mindful that you’re more likely to give in to them when you’re under pressure or need to make quick decisions. If you’re feeling stressed, pause for a minute, collect yourself, and take a few deep breaths.
• Perspective-Taking: If you think you may be stereotyping people or groups, imagine what it would feel like for others to stereotype you.
• Learn to Slow Down: Before jumping to conclusions about others, remind yourself of positive examples of people from their age group, class, ethnicity, or sexual orientation. This can include friends; colleagues; or public figures, such as athletes, members of the clergy, or local leaders.
• Individualization: Remind yourself that all people have individual characteristics that are separate from others within their group. Focus on the things you have in common.
• Check Your Messaging: Instead of telling yourself that you don’t see people based on their color, class, or sexual orientation, learn to use statements that embrace inclusivity. For example, Apple Inc.’s inclusion statement circles around the topic of being different together: “At Apple, we’re not all the same, and that’s our greatest strength.”
• Institutionalize Fairness: In the workplace, learn to embrace and support diversity. The AAFP suggests individuals use the Equity and Empowerment Lens, which is designed to help organizations improve planning and resource allocation to foster more equitable policies.
• Take Two: Overcoming unconscious biases takes time. Understand that this is a lifelong process and that deprogramming your biases requires constant mindfulness and work.

Be Aware and Proactive in Being More Inclusive

Once you’ve identified your personal biases, you can take proactive steps to be more inclusive. For one, check your media bias: Do you find that the blogs you follow, the shows you stream, or the social media accounts you “like” are all produced by people quite similar to you? That kind of affinity is natural, but it also reinforces unconscious biases. Seek out media sources aimed at different groups. You’ll hear challenging opinions and learn how others experience the world.

In the classroom and the workplace, practice intentional inclusion. When asked to form a study or work group, pass over your friends to choose partners from different backgrounds.

Spend Time with People Who Are Different from You

Increasing your contact with different groups can help undermine your subconscious stereotypes.

Societal forces tend to keep us separate from people of different backgrounds and socioeconomic classes. Break out of your usual routine: Join a club sports team or library book group; volunteer with a nonprofit in a different neighborhood; take part in different cultural celebrations (e.g., National Puerto Rican Day, Juneteenth, or Nowruz, the Iranian New Year). This approach is backed by science: Psychological theory suggests that individuals can reduce their prejudices by interacting with people from other races, ethnicities, and backgrounds.

Be Proactive and Take Steps Forward

While implicit bias affects the workplace, school, and social situations, you can work to avoid it through awareness and conscious decision-making. Taking inventory of the biases you have and laying out strategies to overcome them can help lead to a more equitable society for all.

Recommended Reading

7 Tips for Achieving Self-Empowerment

Time Management for Online Students

Job Search Tips: Hard Skills vs. Soft Skills

American Association of Family Physicians, “Eight Tactics to Identify and Reduce Your Implicit Biases”

BuiltIn, “16 Unconscious Bias Examples and How to Avoid Them in the Workplace ”

Catalyst, “11 Harmful Types of Unconscious Bias and How to Interrupt Them”

CIPHR, “What Is Unconscious Bias in the Workplace, and How Can We Tackle It?”

CNN, “Black Men Arrested at Philadelphia Starbucks Reach Agreements”

EnvatoTuts+, “What Is Unconscious Bias? + Top Strategies to Help Avoid It”

Everfi, “How Inclusion in the Workplace Helps Fight Implicit Bias”

Forbes , “Unconscious Bias: How It Affects Us More Than We Know”

Multnomah County, Equity and Empowerment Lens

ONGIG, “10 Examples of Awesome Inclusion Statements”

PLOS ONE, “What Is Creating the Height Premium? New Evidence from a Mendelian Randomization Analysis in China”

Profiles in Diversity Journal, “Overcoming Unconscious Bias Within Organizations”

Project Implicit, Take a Test External link:

Simply Psychology, “Implicit or Unconscious Bias”

ThoughtCo. “What Is the Contact Hypothesis in Psychology?”

VeryWellMind, “How Does Implicit Bias Influence Behavior?”

Vox, “Companies Like Starbucks Love Anti-Bias Training. But It Doesn’t Work and May Backfire”

Bring us your ambition and we’ll guide you along a personalized path to a quality education that’s designed to change your life.

Take Your Next Brave Step

Receive information about the benefits of our programs, the courses you'll take, and what you need to apply.

• How It Works
• All Projects
• Top-rated Pages
• Admission essay writing
• Book report writing
• Coursework writing
• Dissertation writing
• Essay editing
• MBA essay writing
• Scholarship essay writing
• Write my essay
• Free sample essays
• Writing blog

Best Bias Essay Examples

Implicit bias.

308 words | 2 page(s)

1. An implicit bias is a set of attitudes or beliefs regarding a specific group, such as an ethnic group, and age group, or a particular gender. These are not inherently positive or negative associations, but they all tend to be misguided and uninformed. The reason implicit biases are important to understand in regard to health disparities is that having certain biases can create or prolong these biases, due to how they might impact health care. Eliminating implicit biases can help reduce these disparities, resulting in more equitable care for all groups.

2. The IAT I completed was the skin-tone IAT, which included identifying pictures of individuals and then making associations of words that have either a positive or negative connotation. I found the IAT interesting, but I am not certain it identified whether I have any implicit biases of my own, as simply following directions will not reveal any biases. The test seemed to work by identifying any mistakes that would be made.

Use your promo and get a custom paper on "Implicit Bias".

3. The results did not surprise me too much, but I was also methodical in how I approached responses. I am glad that the test results showed I do not have any significant implicit biases regarding skin tone. The mistakes I made were based on uncertainty whether someone was considered light skinned or dark skinned, as there were a few that could be categorized as either.

4. Actions that I can take to mitigate the potential effects of implicit bias would be to not make assumptions about a particular group, whether this includes ethnicity, religion, gender, or age. For instance, one might have an implicit bias that the elderly are incapable of being active, and therefore might not recommend exercise as part of treatment, even if this is the best recommendation. Therefore, communicating with patients individually is the best way to avoid making decisions based on implicit bias.

Have a team of vetted experts take you to the top, with professionally written papers in every area of study.

Alyssa's Blog

Implicit Bias – How do you score?

Implicit bias is the subconscious attitudes and prejudices one has due to prior influences throughout their life, and it is an ongoing concern in the process of recruiting, interviewing, and selecting applicants in all fields. Implicit bias can harm the firm’s ability to select the right applicant, and it can also negatively impact the applicants, who may be on the receiving end of the bias, and have therefore been unintentionally discriminated against by the firm.

So how do we undo this implicit bias, and how can we tell if we’re biased? When conducting an interview, it is a widely held belief that an unstructured interview approach, and a tailored set of questions for each applicant, is the preferred method, however this method is less accurate, and more likely to be biased, than its counterpart, the set question interview method, in which each applicant receives the same questions. This can reduce bias and assist the firm with finding the best applicant for selection.

Now, how do you know if you’re biased? I wondered the same, and I took Harvard University’s Project Implicit test, in the Career and Gender category. This test found that I had a slight bias towards grouping men and career, and women and family. This rings true in my own life, and there were a variety of other options in Project Implicit that are worth trying! Once we know our weaknesses and biases, then we cam begin working to eliminate them, or at least be aware of the implications on our selection processes.

References:

Project Implicit. (2011). Harvard University. https://implicit.harvard.edu/implicit/

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.