Bias & Fairness in AI

Summary

Systematic, unfair skew in model outputs across groups — often inherited from training data — a real risk that requires deliberate testing, not assumption of neutrality.

• 1Test a model with prompts that vary only demographic-related terms and compare outputs.
• 2Learn common sources of bias: training data imbalance, labeling bias, proxy variables.
• 3Read a case study of a real AI fairness failure and what caused it.