Algorithmic Fairness and Social Welfare

Annie Liang and Jay Lu

AEA Papers and Proceedings, 2024, vol. 114, 628-32

Abstract: What constitutes a fair algorithm? In the literature on algorithmic fairness, a common approach is to formulate fairness concerns as statistical constraints and to select the most accurate algorithm satisfying this constraint. This approach is facially distinct from a long tradition in economics based on social welfare, where the utilities of different social identities are aggregated from behind a veil of ignorance. We show that the constrained optimization and social welfare approaches can be fundamentally opposed and propose a framework that nests both approaches as special cases.

JEL-codes: C45 C61 D63 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1257/pandp.20241073

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