FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee

TL;DR

FaiREE is a novel fair classification algorithm that guarantees group fairness under finite samples without relying on distributional assumptions, achieving high accuracy and outperforming existing methods.

Contribution

It introduces a distribution-free, finite-sample fair classification method adaptable to various fairness notions with proven theoretical guarantees.

Findings

FaiREE satisfies group fairness constraints with finite samples.

It achieves optimal accuracy across multiple fairness notions.

Experimental results show superior performance over state-of-the-art algorithms.

Abstract

Algorithmic fairness plays an increasingly critical role in machine learning research. Several group fairness notions and algorithms have been proposed. However, the fairness guarantee of existing fair classification methods mainly depends on specific data distributional assumptions, often requiring large sample sizes, and fairness could be violated when there is a modest number of samples, which is often the case in practice. In this paper, we propose FaiREE, a fair classification algorithm that can satisfy group fairness constraints with finite-sample and distribution-free theoretical guarantees. FaiREE can be adapted to satisfy various group fairness notions (e.g., Equality of Opportunity, Equalized Odds, Demographic Parity, etc.) and achieve the optimal accuracy. These theoretical guarantees are further supported by experiments on both synthetic and real data. FaiREE is shown to have favorable performance over state-of-the-art algorithms.