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Illia Kryvoviaz

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#artificial intelligence Jan 2026

Intersectional Fairness via Mixed-Integer Optimization

It is argued that true fairness requires addressing bias at the intersections of protected groups, and a unified framework that leverages Mixed-Integer Optimization (MIO) to train intersectionally fair and intrinsically interpretable classifiers is proposed.

Jiri Nemecek, Mark Kozdoba, Illia Kryvoviaz et al. · arXiv.org · 0 citations