A Generalised Exponentiated Gradient Approach to Enhance Fairness in Binary and Multi-class Classification Tasks

TL;DR

This paper introduces a Generalised Exponentiated Gradient algorithm to improve fairness in both binary and multi-class classification, addressing a gap in fairness research for multi-class tasks with significant empirical results.

Contribution

It formulates multi-class fair learning as a multi-objective problem and proposes a novel GEG algorithm to enhance fairness under multiple definitions.

Findings

GEG achieves up to 92% fairness improvement.

GEG reduces accuracy by up to 14%.

Outperforms six baseline methods across multiple datasets.

Abstract

The widespread use of AI and ML models in sensitive areas raises significant concerns about fairness. While the research community has introduced various methods for bias mitigation in binary classification tasks, the issue remains under-explored in multi-class classification settings. To address this limitation, in this paper, we first formulate the problem of fair learning in multi-class classification as a multi-objective problem between effectiveness (i.e., prediction correctness) and multiple linear fairness constraints. Next, we propose a Generalised Exponentiated Gradient (GEG) algorithm to solve this task. GEG is an in-processing algorithm that enhances fairness in binary and multi-class classification settings under multiple fairness definitions. We conduct an extensive empirical evaluation of GEG against six baselines across seven multi-class and three binary datasets, using four widely adopted effectiveness metrics and three fairness definitions. GEG overcomes existing baselines, with fairness improvements up to 92% and a decrease in accuracy up to 14%.