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