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