Fairness-Aware Loss Functions and Regularization

A fairness-aware loss adds a weighted penalty for group disparity to the training objective, turning a hard requirement into a tunable preference; its value is the trade-off curve it exposes, its limits are estimation noise, small subgroups and inherited labels.