MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness
For ML practitioners needing a flexible, unified fairness framework, MIFair consolidates diverse fairness notions into one coherent approach, enabling consistent benchmarking and addressing previously unaddressed multi-attribute scenarios.
MIFair introduces a mutual-information-based framework for bias assessment and mitigation that supports intersectionality and multiclass settings, effectively reducing bias across multiple attributes while maintaining predictive performance.
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.