Post-processing fairness with minimal changes

TL;DR

This paper presents a model-agnostic post-processing fairness algorithm that minimally adjusts predictions without needing sensitive attributes at test time, improving fairness while maintaining original model outputs.

Contribution

It introduces a novel minimal-change post-processing method that is model-agnostic and does not require sensitive attributes during testing.

Findings

Effective in reducing bias across multiple datasets

Outperforms four existing debiasing algorithms

Maintains high prediction accuracy

Abstract

In this paper, we introduce a novel post-processing algorithm that is both model-agnostic and does not require the sensitive attribute at test time. In addition, our algorithm is explicitly designed to enforce minimal changes between biased and debiased predictions; a property that, while highly desirable, is rarely prioritized as an explicit objective in fairness literature. Our approach leverages a multiplicative factor applied to the logit value of probability scores produced by a black-box classifier. We demonstrate the efficacy of our method through empirical evaluations, comparing its performance against other four debiasing algorithms on two widely used datasets in fairness research.