MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness

TL;DR

MIFair is a versatile mutual-information-based framework that assesses and mitigates bias in machine learning, effectively handling intersectionality, multiclass, and complex subgroup fairness issues.

Contribution

It introduces a unified, information-theoretic framework for bias measurement and mitigation that supports diverse fairness notions and complex subgroup structures.

Findings

MIFair effectively reduces bias in real-world datasets.

It maintains strong predictive performance while mitigating bias.

Supports intersectionality and multiclass fairness scenarios.

Abstract

Fairness in machine learning remains challenging due to its ethical complexity, the absence of a universal definition, and the need for context-specific bias metrics. Existing methods still struggle with intersectionality, multiclass settings, and limited flexibility and generality. To address these gaps, we introduce MIFair, a unified framework for bias assessment and mitigation based on mutual information. MIFair provides a flexible metric template and an in-processing mitigation method inspired by the Prejudice Remover, defining group fairness as statistical independence between prediction-derived variables and sensitive attributes. We further strengthen its information-theoretic foundation by establishing equivalences with widely used fairness notions such as independence and separation. MIFair naturally supports intersectionality, complex subgroup structures, and multiclass classification and employs regularization-based training to reduce bias according to the selected metric. Its key advantage is its versatility: it consolidates diverse fairness requirements into a single coherent framework, enabling consistent benchmarking and simplifying practical use. Experiments on real-world tabular and image datasets show that MIFair effectively reduces bias, including previously unaddressed multi-attribute scenarios, while maintaining strong predictive performance across the evaluated settings.