Neyman-Pearson and equal opportunity: when efficiency meets fairness in classification

TL;DR

This paper integrates fairness, specifically equal opportunity, into the Neyman-Pearson classification framework to develop classifiers that balance statistical efficiency with social fairness.

Contribution

It introduces the NP-EO framework, deriving the oracle classifier and proposing finite-sample classifiers that ensure fairness and efficiency simultaneously.

Findings

Proposed classifiers satisfy fairness and efficiency constraints with high probability.

Demonstrated effectiveness on simulated datasets.

Validated approach on real-world data.

Abstract

Organizations often rely on statistical algorithms to make socially and economically impactful decisions. We must address the fairness issues in these important automated decisions. On the other hand, economic efficiency remains instrumental in organizations' survival and success. Therefore, a proper dual focus on fairness and efficiency is essential in promoting fairness in real-world data science solutions. Among the first efforts towards this dual focus, we incorporate the equal opportunity (EO) constraint into the Neyman-Pearson (NP) classification paradigm. Under this new NP-EO framework, we (a) derive the oracle classifier, (b) propose finite-sample based classifiers that satisfy population-level fairness and efficiency constraints with high probability, and (c) demonstrate statistical and social effectiveness of our algorithms on simulated and real datasets.