Feature Engineering and Transformation for Fairness

Features carry discrimination into a model even when the protected attribute is excluded; screening for proxies, repairing feature distributions, learning fair representations and constructing better variables reduce it, and every intervention must be validated for fairness, accuracy and intersectional subgroups.