Papers › An Intersectional Definition of Fairness

An Intersectional Definition of Fairness

22 Jul 2018arXiv:1807.08362archive 2025-07-28

James Foulds, Rashidul Islam, Kamrun Naher Keya, SHimei Pan

We propose definitions of fairness in machine learning and artificial intelligence systems that are informed by the framework of intersectionality, a critical lens arising from the Humanities literature which analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including gender, race, sexual orientation, class, and disability. We show that our criteria behave sensibly for any subset of the set of protected attributes, and we prove economic, privacy, and generalization guarantees. We provide a learning algorithm which respects our intersectional fairness criteria. Case studies on census data and the COMPAS criminal recidivism dataset demonstrate the utility of our methods.

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