Classification with abstention but without disparities

TL;DR

This paper introduces a post-processing classification method with abstention that maintains fairness and reduces disparities without requiring labeled data, supported by theoretical guarantees and empirical validation.

Contribution

It formalizes a risk minimization framework for fair abstention and proposes an efficient post-processing algorithm applicable to any score-based classifier using unlabeled data.

Findings

Fairness and abstention constraints can be achieved independently from initial classifier.

The proposed method guarantees risk, fairness, and abstention bounds.

Moderate abstention rates can bypass the typical risk-fairness trade-off.

Abstract

Classification with abstention has gained a lot of attention in recent years as it allows to incorporate human decision-makers in the process. Yet, abstention can potentially amplify disparities and lead to discriminatory predictions. The goal of this work is to build a general purpose classification algorithm, which is able to abstain from prediction, while avoiding disparate impact. We formalize this problem as risk minimization under fairness and abstention constraints for which we derive the form of the optimal classifier. Building on this result, we propose a post-processing classification algorithm, which is able to modify any off-the-shelf score-based classifier using only unlabeled sample. We establish finite sample risk, fairness, and abstention guarantees for the proposed algorithm. In particular, it is shown that fairness and abstention constraints can be achieved independently from the initial classifier as long as sufficiently many unlabeled data is available. The risk guarantee is established in terms of the quality of the initial classifier. Our post-processing scheme reduces to a sparse linear program allowing for an efficient implementation, which we provide. Finally, we validate our method empirically showing that moderate abstention rates allow to bypass the risk-fairness trade-off.