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• W2884003968 abstract "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." @default.
• W2884003968 created "2018-08-03" @default.
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• W2884003968 date "2018-07-22" @default.
• W2884003968 modified "2023-09-24" @default.
• W2884003968 title "An Intersectional Definition of Fairness" @default.
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• W2884003968 doi "https://doi.org/10.48550/arxiv.1807.08362" @default.
• W2884003968 hasPublicationYear "2018" @default.
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