Model Fairness Audit overview

Audits deployed ML models for demographic bias and fairness violations — computing disparate impact ratios, equalized odds, and demographic parity metrics across protected attributes using Fairlearn or AIF360; comparing subgroup performance differentials; documenting mitigation strategies applied (reweighing, threshold adjustment, post-processing); and certifying compliance with organizational fairness policies before production promotion. Excludes model training and feature engineering.