AI Ethics, Governance & Responsible AI
Lesson 2 of 5
0%Lesson 2
20 min
Bias in AI: Sources, Types, and Detection
Quick Summary
Bias can enter through historical data, sampling, labels, features, objectives, and the way a model is deployed. Detecting it requires testing relevant groups and examining who receives each kind of error.
What you will learn
- Describe common sources of AI bias
- Distinguish representation, measurement, and outcome problems
- Plan group-aware testing without assuming one fairness metric is universal
Intermediate Content
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