decision making presentation slides

You are using an outdated browser. Please upgrade your browser to improve your experience.

Decision Making PowerPoint Templates

Our vast library of Decision Making PowerPoint templates are professionally designed to elevate your presentation style in any professional meeting. The collection provides a comprehensive framework to professionals across industries to streamline their efforts, ideas and overall message. The fully editable Decision Making PowerPoint and Google Slides templates breathe life into complex facts, figures and information and present them in an engaging manner to ensure a long lasting impact on your audience’s minds.

Our extensive collection of Decision Making PPT templates promote productivity and convenience when it comes to its applications. These templates have been thoughtfully designed to ensure maximum visual impact. Explore our collection of Decision Making presentation templates and download the perfect template to take your presentation to new heights!

• Price <= $5.99
• Price > $5.99

Business Option PowerPoint Template

Login to use this feature

Add-to-favs lets you build a list for inspiration and future use.

Log in now to start adding your favs.

If you don't have one. A free account also gives you access to our free templates library

Business Options Pros and Cons PowerPoint Template

Problem Solution Directional Arrows PowerPoint Template

Powerpoint Three Levels Rectangle Nodes Tree Org Chart PowerPoint Template

Pricing Options Table PowerPoint Template

Task Completion Status PowerPoint Template

Agenda Slide PowerPoint & Google Slides Template

Pros and Cons Arrows PowerPoint Template

Recommendation PowerPoint Template

Mindmap Flowchart Options PowerPoint Template

swimlane flowchart 01 PowerPoint Template

Business Objectives 2 PowerPoint Template

Decision making powerpoint templates for presentations:.

The Decision Making PowerPoint templates go beyond traditional static slides to make your professional presentations stand out. Given the sleek design and customized features, they can be used as PowerPoint as well as Google Slides templates . Inculcated with visually appealing unique and creative designs, the templates will double your presentation value in front of your audience. You can browse through a vast library of Decision Making Google Slides templates, PowerPoint themes and backgrounds to stand out in your next presentation.

What Is A Decision Making PowerPoint Template?

A Decision Making PowerPoint template is a ready-made presentation template that provides a structured framework for creating professional Decision Making presentations. The Decision Making PPT presentation template includes design elements, layouts, and fonts that you can customize to fit your content and brand.

What Are the Advantages of Decision Making Presentation Templates?

Decision Making PPT presentation templates can be beneficial because they:

• Add multiple visual and aesthetic layers to your slides.
• Ensure that complex information, insights and data is presented in a simplistic way.
• Enhance the overall visual appeal of the content.
• Save you a lot of time as you don’t have to start editing from scratch.
• Improve the professional outlook of your presentation.

How To Choose The Best Decision Making Presentation Templates?

Keep the following points in mind while choosing a Decision Making Presentation template for PowerPoint (PPT) or Google Slides:

• Understand your presentation goals and objectives.
• Make sure the Decision Making template aligns with your visual needs and appeal.
• Ensure the template is versatile enough to adapt to various types of content.
• Ensure the template is easily customizable.

Can I Edit The Elements In Decision Making PowerPoint Templates?

Yes, our Decision Making PowerPoint and Google Slides templates are fully editable. You can easily modify the individual elements including icons, fonts, colors, etc. while making your presentations using professional PowerPoint templates .

Are Decision Making PowerPoint Templates Compatible with Google Slides?

Yes, all our Decision Making presentation templates are compatible and can be used as Decision Making Google Slides templates.

How to Download Decision Making PowerPoint Templates for presentations?

To download Decision Making presentation templates, you can follow these steps:

• Select the resolution (16*9 or 4*3).
• Select the format you want to download the Decision Making template in (Google Slides or PowerPoint).
• Make the payment (SlideUpLift has a collection of paid as well as free Decision Making PowerPoint templates).
• You can download the file or open it in Google Slides.

Related Presentation Templates

Root cause analysis.

68 templates

6 templates

144 templates

5,634 templates

SWOT Analysis

130 templates

Forgot Password?

Privacy Overview

Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information

Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.

Researched by Consultants from Top-Tier Management Companies

Powerpoint Templates

Icon Bundle

Kpi Dashboard

Professional

Business Plans

Swot Analysis

Gantt Chart

Business Proposal

Marketing Plan

Project Management

Business Case

Business Model

Cyber Security

Business PPT

Digital Marketing

Digital Transformation

Human Resources

Product Management

Artificial Intelligence

Company Profile

Acknowledgement PPT

PPT Presentation

Reports Brochures

One Page Pitch

Interview PPT

All Categories

Top 10 Decision-making Templates with Samples and Examples

Nidhi Aswal

In today’s business landscape, decisions drive change! Whether a business thrives in competition or languishes at the bottom depends on the quality and promptness of its decision-making.

Making the right decisions (big or small) while operating a business is crucial to success. Instinct-driven decisions might give you results, but data-driven decisions fuel every successful organization.

Using data to base your decisions on is the in-thing today. You’ll never look at Data-Driven Decision-making the same way after using these  15 PowerPoint Templates ! 

With our collection of Decision-making Templates , you can visually outline strategies and invite stakeholders to provide input and contribute. These PPT Sets range from graphs to mind maps and are an absolute must-have for your professional toolkit. 

The 100% customizable nature of the templates provides you with the desired flexibility to edit your presentations. The content-ready slides give you the much-needed structure.

Let’s take a tour of the PPT Presentations on offer.

Template 1: Consumer Decision-making PPT Deck

Our PPT deck provides you with powerful visualization tools, such as graphs and mind maps, to help you easily understand complex data. Our PPT Bundle on buyer preferences showcases product and approval motives that impact a consumer's purchasing decision. This customer-driven PPT Slideshow offers the advantage of showcasing a real-life use case, making it an excellent choice for your needs. Gain access to our consumer buying desires PowerPoint Template and analyze buyer behavior through performance indicators. Get familiar with cutting-edge concepts, such as getting familiar with a consumer mind-map approach as well. Download now!

Don't wait; grab it now!

Template 2: Problem-solving and Decision-making Preset

Uncover the ultimate solution to your problem-solving and decision-making needs with our expertly crafted 18-slide bundle. Gain a comprehensive grasp of the topic and make informed decisions with ease. Featuring slides with pertinent graphical representations, overviews, subject subtopic presets, and analysis templates are included and ready to be edited, making the product even more valuable. Understand the Kepner-Tregoe Method of situational appraisal in detail with the use of this presentation template. Get it now. 

Get it now!

Template 3: Decision-making Framework Template

With thirteen dynamic presentations, you'll have ample opportunity to explore a diverse range of topics. It helps give an overview and keeps the audience interested when discussing important topics, including decision-making with financial risk evaluation. This PPT Set has a slide that explains the three-step decision-making framework with business goals. It is a resource far too important for you not to have in your toolkit. Get it now.  

Download now!

Template 4: Decision-making Matrix Bundles

Building a decision-making matrix isn’t everyone’s cup of tea. This PPT Template is your savior as it helps build effective ones. It has predesigned frameworks that serve as starting point for constructing a solid decision-making matrix for project and client, business operations, recruitment, product ideas, and more. Download now. 

Get it today!

Template 5: Introduction to Data-driven Decision-making Layout

Use the ideas in this slide to unleash the value and potential of data-driven decision-making. Learn how you can make better business choices using concrete metrics, evident facts, and proper steps. This Data-Driven Decision-Making Layout demonstrates how BI tools in IT can provide you with the knowledge you need to make smarter business choices. Download now.

Template 6: Steps Of Data-driven Decision-making Training Set

This presentation slide describes the process of making decisions based on data. Contextualization, Key Performance Indicators, Visualization, Action Plan, Prioritization of Decision, Execution, Analysis of Results, Learning from Results, and Adjustment of Action Plan are the phases involved. Our team of experts crafted this deck, ensuring that each slide contains relevant and valuable information. Get it today.

Template: 7 Data-driven Decision-making Dashboard 

Here’s a decision-making dashboard that focuses on revenue and customer overview. With insights presented using easy-to-read graphs and charts, making conclusions and strategizing actions become easy. This dashboard highlights average revenue per customer, customer acquisition cost (CAC), CAC vs. customer lifetime value (CLV), and more. Download now.

Template 8: Time Management Benefits of Streamlined Decision-making PPT

This slide highlights the importance of time management in enabling employees to make informed decisions by planning and avoiding last-minute rushes. Understand and internalize the three-step process of managing time and drawing up a schedule; make certain decisions quickly and learn how to make better decisions. An extensive study went into creating this deck's stunning visuals and relevant text. Get it right away.

Template 9: Decision-making Chart PPT Deck

Make your argument clearly with the help of this Decision-making Chart Layout. This resourceful package is adaptable to a broad range of contexts, from product launches and customer purchases to business optimization, construction, and healthcare. You can get all nine slides right now and use them whenever you want. Download now.

Download Now!

Template: 10 Option-Based Decision-Making Alternative Problem Identification Layout

This PPT Theme explains the four-step decision-making process. Its design promotes appropriate discussion on these four steps that are ‘Identifying the Problem,’ ‘Considering Options,’ ‘Selecting an Alternative,’ and ‘Addressing the Effectiveness of the Decision.’ Similarly, our PPT Bundle has slides that offer additional steps that complement the above points, ensuring a more comprehensive approach to decision-making. Download now.

Decide, Deliver, Dazzle

In the realm of decision-making, your presentations need to pack a punch. With our game-changing Decision-Making PPT Templates, you hold the key to captivating your audience, sealing the deal, and leaving a lasting impression. Don't settle for mediocrity when you can soar with excellence. Elevate your presentations to new heights and embrace success with our powerful templates. The choice is yours—make it count!

FAQs on Decision-Making

What are decision-making types.

The four main types of decision-making are:

• Command: Decisions using the command approach are made alone, without consulting or any discussion with others. These options are fraught with unpredictability.
• Consult: In the consultation approach, many people are consulted, but only one person has the authority to make a final choice. 
• Vote: The voting method takes place when everyone can voice their opinion before the speaker calls for a vote. 
• Consensus: By sharing their ideas and listening to those of others, group members, using the consensus approach, eventually come to a decision.

What are the five keys of decision-making?

Here are the five keys to decision-making:

Identification

First, decide what you want. Why are you making that choice? Why is it necessary? Identifying objectives clarifies the situation and aids decision-making.

Seek advice from  the wisest in the group

You don't have to solve everything, even if you're the boss. Talk to someone who has made a similar choice. You must decide whether to fire a talented employee who is outstanding with clients but frequently late or absent. An experienced businessperson can advise you.

Take a call

Determine the proper course of action before making any practical decisions.

Put your plans into action.

Collect information on what's working and what's not.

Related posts:

• 15 PowerPoint Templates that Will Change The Way You Approach Data-Driven Decision Making
• How to Design the Perfect Service Launch Presentation [Custom Launch Deck Included]
• Quarterly Business Review Presentation: All the Essential Slides You Need in Your Deck
• [Updated 2023] How to Design The Perfect Product Launch Presentation [Best Templates Included]

Liked this blog? Please recommend us

Top 10 Business Strategy Framework Templates With Samples and Examples

Top 5 Business Plan Chart Example Templates with Samples

This form is protected by reCAPTCHA - the Google Privacy Policy and Terms of Service apply.

Digital revolution powerpoint presentation slides

Sales funnel results presentation layouts

3d men joinning circular jigsaw puzzles ppt graphics icons

Business Strategic Planning Template For Organizations Powerpoint Presentation Slides

Future plan powerpoint template slide

Project Management Team Powerpoint Presentation Slides

Brand marketing powerpoint presentation slides

Launching a new service powerpoint presentation with slides go to market

Agenda powerpoint slide show

Four key metrics donut chart with percentage

Engineering and technology ppt inspiration example introduction continuous process improvement

Meet our team representing in circular format

7 Easy ways to create decision-making presentations with the help of AI.

David Lavalle | 14 July, 2023

• Introduction

Why are decision-making presentations important for businesses and other domains?

• Take Advantage of the AI-powered presentations today!.
• Every once in a while, we get the opportunity to fascinate someone. It can be anyone, your boss, your team, a sponsor, a benefactor, a group of students, or just random people whom you need to build trust with. You can easily captivate and persuade anyone with your ideas through a well-crafted decision-making presentation!
• At  WeSlides , we create compelling templates and carefully articulated decision-making presentations for the satisfaction of our users. Coming up with an idea and thinking of ways to implement it is hard enough, and you shouldn’t have to waste any precious time dragging and zooming text boxes, fixing font sizes, and stressing over a color theme.
• You can leave the rest to us and focus more on how you would like to present your work. Today, you will get to explore how AI technology can change the way people perceive and react just by adding a few tweaks here and there.

Convincing others is a tough job especially when there’s money at stake. However, using an AI presentation maker can change your prospects of gaining success by a huge percentage.

• Time-Saving

Time is your best friend when it comes to achieving your goals. It can be stubborn and slow for Some but favoring and fast for others. Just think, when it comes to decision-making presentations, it will be nothing but a gift in disguise even if you’re running late! That’s the beauty of having a variety of decision-making presentation templates at the tip of your fingers. Get ahead of time and let the AI tool take care of writing styles, dragging around stuff, building color schemes, and enhancing the viability of your project, particularly when delivering those all-important decision-making presentations.

• Collaborating

These presentations can give you the opportunity to collectively discuss matters with your team and propose better options in the future. It is an excellent way for your team to collectively analyze presented data, statistics, and money management through AI-based presentations.

• Influential

You can never persuade someone without influencing them. A decision-making presentation covers that for you. Gaining support from your audience is your main purpose which is completely fulfilled by using these AI tools.

• Documentation

You can go over these presentations powered by AI a hundred times if you want. You get to store them on the cloud or share it with the world. In return you get full transparency for your audience, references for future analysis, and the option to review and renew anytime!

The 7 ways you can create decision-making presentations.

• Use Natural Language Processing for generating content in decision-making presentations.

Gone are the days when we had to learn the language of zeros and ones to give instructions to a machine/computer. Today you can type your command and let the machine interpret it for you. That’s what an NLP is, and you can utilize it to produce a coherent outcome.

You just punch in a topic, even something complex like decision-making presentations, and it will generate content, summarize it accordingly, and present it to you highlighting the key points as well. A perfect partner for making informed choices. It can also be used as a research tool that can extract data from every nook and corner of the internet with a factual guarantee.

You won’t even have to worry about making your words sound professional, informal/formal, or attractive because an AI PowerPoint presentation maker will also do that for you.

• Get help from visuals to enhance decision-making presentations.

Create exciting and impactful presentations by adding special visual effects .

Colors are more important than you think. That’s why decision-making presentations also help you visualize your output by carving out color themes and palettes according to the topic you provide. It can also be a great tool to psychologically impact the people listening to you. Warm colors instill happiness in the subconscious while dark colors strike mystery, power, and elegance.

The color theme can also play an important role in building your company’s brand color or logo. You can also choose different shades or tints for the same color scheme to portray different aspects of an enterprise’s focus.

Apply predictive analysis and forecast the results while creating decision-making presentations.

You can be at ease with this one because AI develops the path for you including your vision. It’s worth noting that these features are especially handy for crafting decision-making presentations. It will also give you valuable advice along the way and predict possible outcomes. With hundreds of templates available at WeSlides , you can just dial in all the information you have and allow the AI tool to build subtopics and generate a separate section for each topic!

• Leverage AI tools to present well-researched and in-depth information.

With the machine’s judgment and probability of success, you have more focus on communicating with the audience.

Digging out algorithms, unstructured articles, research, graphical representations, reports, sources, and leads and binding them together to build a compelling decision-making presentation can make your workload heavier, thus you should consider utilizing the AI tools to their full extent to blow off some steam.

When you have too much information at hand, you can become stressed out trying to prove it correct. With AI tools you can be a hundred percent assured that all the data and information is factually congruent with your topic.

• Get creative with personalizing your decision-making presentations.

You know your audience best and you have to cater to their needs. Since AI does not have any emotional intelligence, it cannot create an exact replica of what you have in mind. This is where you have the leverage to customize and re-create any template or slide you deem fit.

Feel free to alter the tone of the content, manipulate the use of language, or change font types and sizes. You may also modify some of the information in the text. After all, a machine can’t surpass human intelligence. It’s your unique perspective and approach that will give the text the distinctive touch it needs.

• Collaborate with your team and produce a mutually structured decision-making presentation.

Don’t get stuck with emailing the same thing to your colleagues over and over again. Use AI-based presentations to come together with your team without being present in the same room.

You can post comments on each slide, set reminders, and even chat while making the presentation. Get the confusion out of your hair and communicate with your team effectively.

You can even share your work on social media and view the response rate through the analytics tool . This will provide you with commendable feedback and showcase your weak points so you and your enterprise can enhance them in the future.

• Overcome language barriers and use AI to develop highly interactive decision-making presentations.

Presenting on an international level or educating people in their native language can be tough when creating decision-making presentations because of non-fluency.

However, AI does not have any language barriers. It can respond to any language even if written in slang. You can create your presentation in the native language of your audience or even add a few slides in between with slogans/sayings with their translations to entice traditional values in your viewers.

You can also utilize this tool to extract information from an unknown language to you and present it in the language you are familiar with or the other way around.

Take Advantage of the AI-powered presentations today!

Hope this triggered the innovator inside you, and you are now fully informed of all the ways you can create a decision-making presentation! Artificial Intelligence has made our lives so much easier and more beautiful, yet people are afraid of making the right move.

Work smarter, not harder by multi-tasking with a virtual assistant. You can put all the monotonous, brain-teasing tasks on the shoulders of your AI business presentation maker and exercise delivering your ideas to build rapport with your spectators.

In today’s world, an AI that makes PowerPoint presentations is the only ligament that saves us time, effort, energy, and useless meetings on who gets to create what part of a presentation. You can run a whole organization or just be fine with a one-man show, weslides.com will make sure to give you a gratifying outcome with defying results in the end.

• Help Center
• Terms & Condition
• Privacy policy

Home Blog Business A Lowdown on Decision-Making for Business Leaders

A Lowdown on Decision-Making for Business Leaders

You’ve got an important decision to make. But instead of cooperating, your brain goes into chaos mode, bombarding you with conflicting thoughts and loads of doubts. At the same time, the clock is ticking, raising your anxiety levels even further. Sounds familiar? 

Most of us are not naturally good at decision making. But we can train our brain to churn out good decisions on command even in tough situations. This post will explain exactly how our decision making process works and what can you do to become better at each step of it. 

What is Decision Making? 

Every day we make a myriad of choices — from what to have for breakfast to how to better plan a project. Sometimes we operate with sufficient knowledge and data. Other times, we rely more on intuition and hope that we are doing the right thing.

Dartmouth provides the following decision making definition: 

Decision making is the process of making choices by identifying a decision, gathering information, and assessing alternative resolutions. 

Our day-day decisions rarely require more than a minute of analysis. Their consequences and impact are easy to gauge too. However, in business settings, every decision can have a long-lasting impact, pertaining to different people, processes, and outcomes. Hence, knowing how to collect, analyze, and act on data, rather than on a hunch is key to driving consistent, desirable results. 

Types of Decision Making 

Every decision-making process is based on data — our past knowledge, experiences, opinions, emotions, and biases developed. Rarely if ever, decisions are made outside of a wider context. Respectively, both internal and external contexts dictate the types of decisions we have to make.

The most basic classification is instant vs informed decision making:

Instant decisions

Are daily, nearly automatic responses to certain events. We make these based on our emotions and past knowledge of what worked and what didn’t. 

Informed decisions

Represent our rational decision making abilities. These are mostly driven by our critical thinking, empirical evidence, and problem-solving skills. 

Types of Decision Making Models in Business 

Such data-driven decision-making is more common in work settings. Respectively, the decisions leaders take every day can be classified into three groups :

Furthermore, the decision-making model can be also classified as centralized vs decentralized decisions. A centralized decision making process occurs when senior executives and stakeholders take most decisions. However, decentralized (or shared decision making) means that leaders at different levels of the organization have the authority to make individual decisions. 

The 7 Step in Decision Making Process 

There’s a multitude of decision-making styles and approaches, but ultimately every type of successful decision boils down to following the next steps. 

1. Identify the Problem

Our brain is naturally puzzled when presented with an unclear goal. Therefore, instead of acting rationally, we procrastinate, engage in excessive information gathering, or let decision making biases trump the logic. 

Don’t dwell too long on trying to collect as much information as possible. In most cases, you’ll just get stuck with analysis paralysis. Instead, spend more time articulating the problem at hand, so that you could find the right information to back up your decision. Or in words of Albert Einstein:

If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute resolving it.

Oftentimes, business leaders think backward — from the outcome to the solution and then the problem. Such a line of thinking often leads to overlooking more creative and elegant solutions that could lead to a similar or even better outcome. Always make a specific problem the focal point of your thinking. When analyzing a solution, it can also be a good idea to share the advances or findings with your team through a presentation with valuable progress.

2. Collect Information 

You can’t make a good decision without sufficient data. Once you have a formalized problem statement, you need to find sufficient points for making the optimal decision. Several of our PowerPoint templates and slides mentioned in this article provide an excellent path to understanding some of that relevant info to collect. Also, you will find some great questions to ask yourself and your team.

The consensus-decision-making process will seek those evidence from other participants. More independent and authoritative leaders will rely more on personal expertise and hard numbers.

As per usual, the best approach lies somewhere in the middle — use both personal judgments and relevant data from others. Whether you are analyzing evidence provided by others or one’s you’ve amassed personally, always ask the following questions: 

• What is the source of the information? Is it authoritative and trustworthy? 
• How old is the data? Is it still relevant in the current market conditions?
• What sources were used to produce this information? 
• Does the source make any unrealistic claims? 

The above are baseline fact-checking best practices that should be applied in decision making in management.

3. Assess the Alternatives 

Based on the information you’ve gathered, create a simple decision-making matrix to analyze different paths of action. Develop a set of scenarios, but don’t rush to classify them as “best”/“worst” immediately. Instead, try to determine how fast each route could help you reach your goal and what would be the tradeoffs. You can also decide if this is a good moment to share through a presentation with stakeholders the steps taken and the action plan moving forward.

Remember: ethical decision making model does not always mean taking the easiest route, but rather pursuing one that is good both for your business and the people you serve. 

Here are two helpful PowerPoint templates to help you at this stage:  Decision tree diagram for PowerPoint and Sales Tree Templates .

4. Weigh the Evidence

After the analysis session, you’d probably have a list of more favorable alternatives. List them up in priority order and then take another look at the evidence you’ve collected earlier.

At this stage, your main goal is to minimize the impact of personal bias on the decision. Our brain is a fickle instrument that can interpret the same information in different ways. Recent laboratory studies , for instance, found that different people can interpret the same data differently, based on their socio-political beliefs. For example, 68% of Republicans decided that a group of people on the videotape were a riot, whereas only 30% of Democrats thought the same. In reality, the tape demonstrated a peaceful protest.

Acknowledging and eliminating such bias can be challenging. For that reason, when making hard decisions, seek second opinions or leverage extra tools for drawing conclusions. For example, a 2×2 Matrix Quadrant can help you plot various evidence along the axis to better assess the information. 

5. Select Among the Alternatives 

Once you’ve done with examining all the evidence, settle on the scenario that appears as the best choice for you. If you don’t have the ultimate contender at this point, refine your choice using one of the popular decision making theories.

When working with your team, a good approach is to use the PowerPoint presentation templates like the Eisenhower Decision Matrix which provides a sense of urgency in the decision making. Also, the Vroom-Yetton decision model considers how many people should be involved in a decision, if it represents an individual matter or a group one.

Circle of competence

Popularized by Warren Buffett, this framework encourages you to always stay within your area of competence aka what you know best. If you recognize that a certain scenario can lead you into an area when you lack skills or knowledge, pick another route that lets you stay in your lane and capitalize on your strengths. 

6. Take Action

If you’ve completed all of the above steps, you’ll have a detailed plan of action. Now you’ll need to follow through and walk down the selected path. Making decisions isn’t always the hardest part. Acting on them can be much more challenging, especially if your decision assumes significant changes. In this stage, you can ask yourself: How will I share this with the team? A vital tip is to build a presentation that will guide the implications and changes a decision might represent to an organization.

As you plan the execution, consider how you will handle change management and potential resistance. Also, account and address potential risks and put down mitigation plans for those. 

7. Review Your Decision and the Consequences

Once the deed is done and you’ve completed the execution, analyze whether your actions helped resolve the problem formulates at Step 1. If the outcomes didn’t fully address the problem, you may need to repeat some of the earlier steps and consider following the next-best alternative.

To better understand why your decision didn’t work best, consider doing an after-action review — once a military practice, now used by well-performing teams to assess how the actual results differ from the expected ones, what’s the reason for that, and how we can rectify the outcomes

Alternatively, you can use another assessment tool such as the Action evaluation matrix PowerPoint templates which even thou is more geared towards problem-solving effectiveness, can be useful to decide the urgency of each action. A great feature of this matrix is that it allows the possibility to analyze problems and solutions and create a root cause analysis and potential understanding workarounds.

Other Popular Decision-Making Models and Frameworks

For years, different researchers have attempted to discern what makes good decision making and how business leaders can get better at calling the right shots. 

Consequently, many different decision making frameworks have emerged, on top of those we have discussed so far. 

If you are still struggling to make the right decision at this point, try to apply the following tactics.

Also developed by the US military, this framework prompts you to use a cyclic approach to decision making. First, observe the current situation and determine how different stressors shape it. Then, orient yourself towards recognizing all the shaping forces and try to eliminate any influences or bias from your thinking. Next, decide on the best course of action based on the information you have and the analysis you did. Finally, act on the selected course. If the first decision led to a subpar outcome, repeat the OODA cycle again. 

Recognition-Primed Decision Model

An intuitive decision-making model that prompts you to make a quick decision in a complex setting. First, dwell on the problem — gather input, data, and opinions; formalize the outcomes. Think if you have encountered similar situations before and if you could apply the same resolution to this one. Then, analyze the information. Do a mental simulation of different scenarios. Focus on determining the practicalities of each option. If you feel that you don’t have enough data, go back to step 1. Otherwise, proceed with the decision implementation.

Ladder of Inference

In this case, you are prompted to take your decision through seven “rungs” in the ladder, representing your thought process. These include: general facts, selected reality (the context of your decision), interpreted reality, assumptions, conclusions, beliefs, and actions. 

If you want to select additional decision making PowerPoint templates you can check our gallery , and scroll for the diagram that fits your need.

To Conclude 

No one is born with stellar decision making skills. The best leaders develop them over time by accumulating experience, applying different decision-making frameworks, and learning to recognize their biases. You too can get better at making decisions by using the tips and tools, outlined in this post! 

1. 5 Step Decision-Making and Problem-Solving Diagram for PowerPoint

Learn more about the 5 Step Problem Solving technique; this framework allows businesses to make informed and structured decisions in an intuitive 5 step format.

Use This Template

Like this article? Please share

Business Diagrams, Business PowerPoint Templates, Business Presentations, Decision Making, Decision Tree, Leadership Filed under Business

Related Articles

Filed under Business • February 7th, 2024

How to Create & Present a Competitive Landscape Slide for Your Pitch Deck

Get to know how to properly create a winning competitive landscape slide for your pitch deck. Boost your pitch performance now.

Filed under Business • February 2nd, 2024

Business Plan Presentations: A Guide

Learn all that’s required to produce a high-quality business plan presentation in this guide. Suggested templates and examples are included.

Filed under Business • January 31st, 2024

How to Create a Sponsorship Deck (Guide + Examples)

Impress your audience and secure deals by knowing the insights on how to create a winning Sponsorship Deck. Step-by-step instructions + templates.

Leave a Reply

Dr.’s Substack

Unlock the Power of Data Visualization: Integrating MS Power BI report in MS PowerPoint

Integrating Microsoft Power BI into PowerPoint enhances presentations with real-time data insights and interactive visuals. This powerful combination improves data storytelling, simplifies complex information, and maintains data accuracy. By embedding Power BI reports in PowerPoint slides, users create dynamic and engaging presentations that drive informed decision-making and better business outcomes. In this blog, we will explore how you can leverage the power of MS Power BI within MS PowerPoint to create compelling and impactful presentations that effectively communicate your data-driven insights.

Introduction

In this digital age, data is the lifeblood of organizations, and making informed decisions based on accurate data is crucial for success in today's competitive business landscape. Microsoft Power BI has emerged as one of the leading business intelligence tools, empowering users to analyze and visualize data in a meaningful way. However, effectively communicating data insights to stakeholders is equally important. This is where the integration of Power BI into Microsoft PowerPoint comes into play. By embedding interactive Power BI reports and visuals directly into PowerPoint slides, users can create dynamic and engaging presentations backed by real-time data insights. This powerful combination enables presenters to effectively communicate complex data in an easily digestible format, maintain a consistent narrative, and ensure data accuracy, ultimately driving informed decision-making and better business outcomes.

Thanks for reading Dr.’s Substack! Subscribe for free to receive new posts and support my work.

Integrating MS Power BI in MS PowerPoint

There are different methods for integrating Microsoft Power BI into Microsoft PowerPoint. One of these methods is 'Embed Live Data'.

Embed Live Data

Step 1: access microsoft power bi service.

Navigate to Microsoft Power BI Service by entering the following URL: 'https://app.powerbi.com.' Select the workspace containing your report and choose the report.

Upon selecting the report, the Microsoft Power BI report will open.

Step 2: Export the Power BI Report to PowerPoint

For the active Power BI Page click on the 'Export' option, select 'PowerPoint,' and then choose 'Embed Live Data.'

A window titled 'Embed Live Data in PowerPoint' will appear. You can either copy the report page link and paste it into PowerPoint, or you can directly open the report in PowerPoint by clicking on 'Open in PowerPoint'.

The MS PowerPoint app will launch. Click on 'Accept and Continue' to accept the MS Power BI add-on license agreement for MS PowerPoint

The Power BI report will be displayed in MS PowerPoint.

Step 3: Filtering the Power BI Report within PowerPoint

Now you can filter the same way as you do in MS Power BI. Here, I am filtering by 'Products'.

Step 4: Storytelling in PowerPoint

Recently, in the MS Power BI April 2024 update , Microsoft has improved the storytelling in PowerPoint when we’re integrating MS Power BI report.

In fact, the process of saving Power BI content as an image by adding a new dropdown menu in the add-in's footer has been added.

You can now easily switch between live data and snapshot views. For snapshots, there are two options: public (visible to anyone who can view the presentation) and private (only visible to those with permission to view the report in Power BI).

Click on 'Live Data' located at the bottom left of your Power BI report.

A dropdown list will appear.

A dropdown menu will be displayed and choose the option that you want.

Step 5: The remaining Power BI Pages in PowerPoint

Choose another page within the MS Power BI, click on the 'Export' option, select 'PowerPoint,' and then choose 'Embed Live Data.'

A window titled 'Embed Live Data in PowerPoint' will appear. Now copy the report page link.

Step 6: Paste the copied Power BI page in PowerPoint

Switch back to PowerPoint, add a new slide, and choose the 'Add-ins' button.

And choose the Microsoft Power BI Add-ins.

Paste the Copied URL in the textbox and click on 'Insert'.

The Power BI Report page will be displayed.

In conclusion, integrating Microsoft Power BI into PowerPoint provides numerous benefits, such as real-time data insights, interactive visuals, and improved data accuracy. This results in dynamic and engaging presentations that drive informed decision-making and better business outcomes. By following the steps in this blog, you can leverage the power of MS Power BI within MS PowerPoint to create compelling and impactful presentations that effectively communicate your data-driven insights.

Ready for more?

• Open access
• Published: 18 April 2024

Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

• James Shaw 1 , 13 ,
• Joseph Ali 2 , 3 ,
• Caesar A. Atuire 4 , 5 ,
• Phaik Yeong Cheah 6 ,
• Armando Guio Español 7 ,
• Judy Wawira Gichoya 8 ,
• Adrienne Hunt 9 ,
• Daudi Jjingo 10 ,
• Katherine Littler 9 ,
• Daniela Paolotti 11 &
• Effy Vayena 12  

BMC Medical Ethics volume  25 , Article number:  46 ( 2024 ) Cite this article

Metrics details

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Peer Review reports

Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table  1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table  2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

Clark P, Kim J, Aphinyanaphongs Y, Marketing, Food US. Drug Administration Clearance of Artificial Intelligence and Machine Learning Enabled Software in and as Medical devices: a systematic review. JAMA Netw Open. 2023;6(7):e2321792–2321792.

Article   Google Scholar  

Potnis KC, Ross JS, Aneja S, Gross CP, Richman IB. Artificial intelligence in breast cancer screening: evaluation of FDA device regulation and future recommendations. JAMA Intern Med. 2022;182(12):1306–12.

Siala H, Wang Y. SHIFTing artificial intelligence to be responsible in healthcare: a systematic review. Soc Sci Med. 2022;296:114782.

Yang X, Chen A, PourNejatian N, Shin HC, Smith KE, Parisien C, et al. A large language model for electronic health records. NPJ Digit Med. 2022;5(1):194.

Meskó B, Topol EJ. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. NPJ Digit Med. 2023;6(1):120.

Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nat Mach Intell. 2019;1(9):389–99.

Minssen T, Vayena E, Cohen IG. The challenges for Regulating Medical Use of ChatGPT and other large Language models. JAMA. 2023.

Ho CWL, Malpani R. Scaling up the research ethics framework for healthcare machine learning as global health ethics and governance. Am J Bioeth. 2022;22(5):36–8.

Yeung K. Recommendation of the council on artificial intelligence (OECD). Int Leg Mater. 2020;59(1):27–34.

Maddox TM, Rumsfeld JS, Payne PR. Questions for artificial intelligence in health care. JAMA. 2019;321(1):31–2.

Dzau VJ, Balatbat CA, Ellaissi WF. Revisiting academic health sciences systems a decade later: discovery to health to population to society. Lancet. 2021;398(10318):2300–4.

Ferretti A, Ienca M, Sheehan M, Blasimme A, Dove ES, Farsides B, et al. Ethics review of big data research: what should stay and what should be reformed? BMC Med Ethics. 2021;22(1):1–13.

Rahimzadeh V, Serpico K, Gelinas L. Institutional review boards need new skills to review data sharing and management plans. Nat Med. 2023;1–3.

Kling S, Singh S, Burgess TL, Nair G. The role of an ethics advisory committee in data science research in sub-saharan Africa. South Afr J Sci. 2023;119(5–6):1–3.

Google Scholar  

Cengiz N, Kabanda SM, Esterhuizen TM, Moodley K. Exploring perspectives of research ethics committee members on the governance of big data in sub-saharan Africa. South Afr J Sci. 2023;119(5–6):1–9.

Doerr M, Meeder S. Big health data research and group harm: the scope of IRB review. Ethics Hum Res. 2022;44(4):34–8.

Ballantyne A, Stewart C. Big data and public-private partnerships in healthcare and research: the application of an ethics framework for big data in health and research. Asian Bioeth Rev. 2019;11(3):315–26.

Samuel G, Chubb J, Derrick G. Boundaries between research ethics and ethical research use in artificial intelligence health research. J Empir Res Hum Res Ethics. 2021;16(3):325–37.

Murphy K, Di Ruggiero E, Upshur R, Willison DJ, Malhotra N, Cai JC, et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics. 2021;22(1):1–17.

Teixeira da Silva JA. Handling ethics dumping and neo-colonial research: from the laboratory to the academic literature. J Bioethical Inq. 2022;19(3):433–43.

Ferryman K. The dangers of data colonialism in precision public health. Glob Policy. 2021;12:90–2.

Couldry N, Mejias UA. Data colonialism: rethinking big data’s relation to the contemporary subject. Telev New Media. 2019;20(4):336–49.

Organization WH. Ethics and governance of artificial intelligence for health: WHO guidance. 2021.

Metcalf J, Moss E. Owning ethics: corporate logics, silicon valley, and the institutionalization of ethics. Soc Res Int Q. 2019;86(2):449–76.

Data Protection Act - OFFICE OF THE DATA PROTECTION COMMISSIONER KENYA [Internet]. 2021 [cited 2023 Sep 30]. https://www.odpc.go.ke/dpa-act/ .

Sharon T, Lucivero F. Introduction to the special theme: the expansion of the health data ecosystem–rethinking data ethics and governance. Big Data & Society. Volume 6. London, England: SAGE Publications Sage UK; 2019. p. 2053951719852969.

Reisman D, Schultz J, Crawford K, Whittaker M. Algorithmic impact assessments: a practical Framework for Public Agency. AI Now. 2018.

Morgan RK. Environmental impact assessment: the state of the art. Impact Assess Proj Apprais. 2012;30(1):5–14.

Samuel G, Richie C. Reimagining research ethics to include environmental sustainability: a principled approach, including a case study of data-driven health research. J Med Ethics. 2023;49(6):428–33.

Kwete X, Tang K, Chen L, Ren R, Chen Q, Wu Z, et al. Decolonizing global health: what should be the target of this movement and where does it lead us? Glob Health Res Policy. 2022;7(1):3.

Abimbola S, Asthana S, Montenegro C, Guinto RR, Jumbam DT, Louskieter L, et al. Addressing power asymmetries in global health: imperatives in the wake of the COVID-19 pandemic. PLoS Med. 2021;18(4):e1003604.

Benatar S. Politics, power, poverty and global health: systems and frames. Int J Health Policy Manag. 2016;5(10):599.

Download references

Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

Author information

Authors and affiliations.

Department of Physical Therapy, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada

Berman Institute of Bioethics, Johns Hopkins University, Baltimore, MD, USA

Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

Department of Philosophy and Classics, University of Ghana, Legon-Accra, Ghana

Caesar A. Atuire

Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

Phaik Yeong Cheah

Berkman Klein Center, Harvard University, Bogotá, Colombia

Armando Guio Español

Department of Radiology and Informatics, Emory University School of Medicine, Atlanta, GA, USA

Judy Wawira Gichoya

Health Ethics & Governance Unit, Research for Health Department, Science Division, World Health Organization, Geneva, Switzerland

Adrienne Hunt & Katherine Littler

African Center of Excellence in Bioinformatics and Data Intensive Science, Infectious Diseases Institute, Makerere University, Kampala, Uganda

Daudi Jjingo

ISI Foundation, Turin, Italy

Daniela Paolotti

Department of Health Sciences and Technology, ETH Zurich, Zürich, Switzerland

Effy Vayena

Joint Centre for Bioethics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada

You can also search for this author in PubMed   Google Scholar

Contributions

JS led the writing, contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. CA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. PYC contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AE contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JWG contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AH contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DJ contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. KL contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DP contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. EV contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper.

Corresponding author

Correspondence to James Shaw .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

Download citation

Received : 31 October 2023

Accepted : 01 April 2024

Published : 18 April 2024

DOI : https://doi.org/10.1186/s12910-024-01044-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Artificial intelligence
• Machine learning
• Research ethics
• Global health

BMC Medical Ethics

ISSN: 1472-6939

• General enquiries: [email protected]

Got any suggestions?

We want to hear from you! Send us a message and help improve Slidesgo

Top searches

Trending searches

49 templates

18 templates

32 templates

42 templates

40 templates

16 templates

Decision Making Models Infographics

Free google slides theme and powerpoint template.

In need of some infographics about decision making models? Here’s an easy decision: download this template! Its wide variety of colorful and easy to understand infographics make it a breeze to present complex information in a straightforward manner while keeping your audience involved. Tables, charts, graphs… they’re all here for your convenience. So customize to fit your needs and you’ll be on your way to success. Talk about an easy decision!

Features of these infographics

• 100% editable and easy to modify
• 31 different infographics to boost your presentations
• Include icons and Flaticon’s extension for further customization
• Designed to be used in Google Slides, Microsoft PowerPoint and Keynote
• 16:9 widescreen format suitable for all types of screens
• Include information about how to edit and customize your infographics

How can I use the infographics?

Am I free to use the templates?

How to attribute the infographics?

Attribution required If you are a free user, you must attribute Slidesgo by keeping the slide where the credits appear. How to attribute?

Related posts on our blog.

How to Add, Duplicate, Move, Delete or Hide Slides in Google Slides

How to Change Layouts in PowerPoint

How to Change the Slide Size in Google Slides

Related presentations.

Premium template

Unlock this template and gain unlimited access

Register for free and start editing online

Free All-in-One Office Suite with PDF Editor

Edit PDFs like Word

Convert, merge, split, scan, and sign PDFs

More than PDF: Use Word, Excel, and PPT for FREE

Windows • MacOS • Linux • iOS • Android

• Articles of PDF
• PDF Tutorials

how to professionally write or create quarterly report in Word 2022

Regardless of the size, your financial department is the backbone of your organization. To make financial fluency, a business demands smart decision-making in order to stand out in the fierce competition. Decisions regarding budgeting and marketing prospects are pretty much important. Any business needs properly planned marketing parameters rather than relying on luck and free opportunity.

This blog will guide you to make quarterly reports which will help in creating your own statements to match it with the pace of your first-row competitors. Whether it is a small or a large company, these (reports) are an effective way to get an explorative view of your operations, goals, strategic requirements and track the overall company status.

How to Write Quarterly Report in Word 2022

Steps to create quarterly report in word.

• Open the Word app and search for the annual report in the search bar.
• Then click on any layout or style you like.
• After this, a pop-up window will show-up.
• Click on the create option.
• Now fill all the information in relevant blanks..
• Save the report, then a Word report File will be created on your PC.

Advantages of Writing Quarterly Report

• A report provides combined and updated information of three months passed, in a factual way of a specific subject matter
• Based on Quarterly reports, any business can plan things in an effective and efficient manner
• Since these quarterly reports offer 90 days reliable data, they can also be used in the decision making process for another three months
• Quarterly reports are prepared after a lot of research and gathering three month’s data. Due to this, the whole company examines the real achievements and challenges.
• These reports are also a rescue door for solving various kinds of problems faced in the past three months
• Along with solving past-problems, another good thing is its ease of comprehension
• Since reports are facilitating planning and decision making, they are regarded as highly effective

WPS Office - The Best App To Write Quarterly Reports

If you're searching for a way to improve your reporting, look not far than WPS Office. This software can help you create daily, weekly, monthly and quarterly reports and essays efficiently. Besides this, you can also work in other types of documents such as PDF and Excel ect, with ease.

• To make a wonderful quarterly report in WPS Office, click the “+” menu option, then, select New and then Document.
• You'll be asked to name your document, after that, you'll need to choose a template to proceed. Here you can choose a quarterly work report under the Work tab.

Once you've clicked on a template, you'll just need to fill in the information like:

• Provide an overview of the quarterly project
• List the sources used, in detail
• Once it is done, the next step is to start writing
• Start by outlining your information in brief paragraphs
• Provide detailed information in each paragraph
• Wrap up your report by addressing FAQs
• Proofread your report

WPS Office is a productive option to improve your writing skills without even paying a single penny. Try it once and see how easy it is to create unique documents. Download the WPS Office app now.

• 1. Read how to write or create weekly report in Word 2022
• 2. Learn how to write or create monthly report in Word 2022
• 3. Guide on how to write or create daily report in Word 2022
• 4. Guide on How to Write or Create Annual Report in Word 2022
• 5. How to Use QUARTERLY Formula in Excel (Clear Steps With Examples)
• 6. how to write or create quarterly report in Excel 2022

15 years of office industry experience, tech lover and copywriter. Follow me for product reviews, comparisons, and recommendations for new apps and software.