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  • title: An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning
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            An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning
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            An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning

            Dec 6, 2021

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            Cyrus Cousins

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            We address an inherent difficulty in welfare-theoretic fair ML, propose an alternative, and study the resulting computational and statistical learning questions. Welfare metrics quantify overall wellbeing across a population of groups, and welfare-based objectives and constraints have recently been proposed to incentivize fair ML methods to satisfy their diverse needs. However, many ML problems are cast as loss minimization, rather than utility maximization tasks, thus requiring non-trivial mode…

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            NeurIPS 2021

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