Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing

Understand

Machine learning algorithms may have disparate impacts on protected groups.

• To address this, we develop methods for Bayes-optimal fair classification, aiming to minimize classification error subject to given group fairness constraints.
• We introduce the notion of \emph{linear disparity measures}, which are linear functions of a probabilistic classifier; and \emph{bilinear disparity measures}, which are also linear in the group-wise regression functions.
• We show that several popular disparity measures -- the deviations from demographic parity, equality of opportunity, and predictive equality -- are bilinear.