Beginner level · Module 5: AI Ethics and Responsible Use · Lesson 1 of 3
Bias and Fairness
Understand how AI systems can treat people unfairly and what can be done about it.
In this lesson you will
- Explain how bias enters AI systems
- Identify fairness risks in an AI use
Key concepts
biased dataunequal performancefairnesshuman oversight
The lesson
AI learns from data that reflects the past, including its unfairness. A hiring tool trained on past hiring may favour the groups hired before.
AI may also work less well for groups under-represented in its data — certain accents, languages, skin tones or rural settings.
Responsible use means asking who could be disadvantaged, testing the system for different groups and keeping people responsible for decisions that affect others.
Step by step
- Read a short case of biased AI.
- Identify where the bias came from.
- Identify fairness risks in one of your AI uses.
- Propose checks.
Practice activity
Write the fairness section of the AI Use Guide.
Expected result: A clear section with realistic risks and checks.
Check your understanding
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How can past data make AI unfair?
Show answer
It reflects past decisions, including unfair ones, which the AI learns.
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Who should be responsible for decisions that affect people?
Show answer
People, with AI as support.
Taught live by an instructor — online or in person. Free course lessons are for self-study and do not lead to a certificate — see registration options.