The Intersectionality Problem for Algorithmic Fairness

Johannes Himmelreich, Arbie Hsu, Ellen Veomett, Kristian Lum

Proceedings of Machine Learning Research, May 2025

Johannes Himmelreich

Public Administration & International Affairs Department

Autonomous Systems Policy Institute

Campbell Public Affairs Institute

Research Methods

Proceedings of Machine Learning Research

Johannes Himmelreich

A yet unmet challenge in algorithmic fairness is the problem of intersectionality, that is, achieving fairness across the intersection of multiple groups—and verifying that such fairnesshas been attained. Because intersectional groups tend to be small, verifying whether a model is fair raises statistical as well as moral-methodological challenges.

This paper (1) elucidates the problem of intersectionality in algorithmic fairness, (2) develops desiderata toclarify the challenges underlying the problem and guide the search for potential solutions, (3) illustrates the desiderata and potential solutions by sketching a proposal using simplehypothesis testing, and (4) evaluates, partly empirically, this proposal against the proposed desiderata.