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Co-funded by the European Union. Views and opinions expressed are however those of the author or authors only and do not necessarily reflect those of the European Union or the Foundation for the Development of the Education System. Neither the European Union nor the entity providing the grant can be held responsible for them.

Module 1 – Fairness and Bias in AI

Module 1's overall obective

Module 1 explores how bias can enter AI systems, why it can lead to unfair or unbalanced results, and what we can do about it. You will learn how to question AI-generated content, recognise different forms of bias and make more informed, fair and responsible choices when using AI.

Before You Start...

Estimated time between 70-85 minutes

Available formats include View online (flipbook) or download as PDF

Includes practical examples, discussion activities and exercises that help you recognise bias in real AI use

Designed for young people aged 15–30, youth workers and educators

You can complete this module on its own or as part of the full Planet Pulse "Youth Rising Above Climate Anxiety" course.

What you'll learn

1

What fairness and bias mean in AI, and why unfair AI outputs can have real consequences

2

How bias can enter AI through training data, selection, model design, human decisions and the way questions are asked

3

Five practical rules for using AI more fairly and responsibly

4

How to recognise stereotypes, missing perspectives and unequal representation in AI-generated content

5

How to question, check and improve AI outputs using more inclusive prompts, reliable sources and critical thinking

Access the Module and digital badge

View online below via the interactive flipbook

Download PDF using the button below to browse offline

Put your learning into practice and earn the Fairness & Bias in AI Digital Badge.

Complete the module and the Module 1 knowledge check below to demonstrate your understanding of fairness, bias and responsible AI use.

Your badge recognises your progress in building the practical and ethical AI skills promoted through the VALUES programme.

Quiz

1. What does 'bias' mean in the context of AI?(Required)
2. Which of the following is an example of training data bias?(Required)
3. According to the five simple rules in Module 1, what should you do before using AI on important information?(Required)
4. What is 'selection bias' in AI?(Required)
5. Rule 4 of the five rules says that humans should always supervise AI. Why is this especially important?(Required)
6. What is 'post-deployment bias' or feedback loop bias?(Required)
7. According to the Amazon AI hiring case study, what mistake did the AI system make?(Required)
8. How can you reduce bias when writing prompts for an AI image or text generator?(Required)
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