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TeacherColab · Free Student Worksheet
What Is AI Bias? Fairness in Artificial Intelligence
Student Name
Date
Learning Objective
Explore why AI can be biased, how biased training data causes unfair predictions, and what engineers do to build fairer AI systems.
Section 1 — What do you already know?
Before we begin, write down anything you already know about: AI Bias & Fairness
Section 2 — Key Concepts
1. What is AI bias?
Answer:
AI bias happens when an AI system produces results that unfairly favour or disadvantage certain groups of people. It usually occurs because the training data reflects human biases or does not represent all groups equally.
2. How does biased training data cause problems?
Answer:
AI learns patterns from training data. If the training data shows more examples of one group, or reflects historical discrimination, the AI will learn and repeat those biased patterns in its predictions.
3. Can you give an example of AI bias?
Answer:
Facial recognition systems have been shown to misidentify people with darker skin tones more often than lighter ones — because training datasets contained more images of lighter-skinned people. This can cause serious harm if used in security or law enforcement.
Section 3 — Check Your Understanding
Answer the following questions in your own words.
1. Define AI bias and explain how it occurs
2. Identify how biased training data leads to unfair outcomes
3. Give real examples of AI bias causing harm
4. Explain three strategies to reduce AI bias
Section 4 — Fill in the Blanks
Complete the sentences using the concepts you have learned.
1. AI Bias & Fairness helps computers _________________ patterns from _________________.
2. The main difference between AI and a normal program is that AI _________________ from data rather than following fixed _________________.
3. One real-world example of AI Bias & Fairness is _________________, which uses it to _________________.
4. The inputs used by an AI model are called _________________ and the output it predicts is called a _________________.
Section 5 — Scenario Thinking
🤔 Think about this:
A healthcare organisation wants to use AI Bias & Fairness to assist doctors in diagnosing patients earlier. What data would the AI need? What challenges could arise? How can the team ensure the AI is fair, safe, and accurate?
Section 6 — What Did I Learn?
✅ Three key ideas I now understand:
❓ One question I still have:
⭐ Challenge Task
If you were designing an AI solution using AI Bias & Fairness, what problem would you solve? Outline a 3-step plan: (1) Problem statement, (2) Data to collect, (3) How to verify accuracy.