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Algorithmic Fairness and Social Welfare

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  • Annie Liang
  • Jay Lu

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.

Suggested Citation

  • Annie Liang & Jay Lu, 2024. "Algorithmic Fairness and Social Welfare," AEA Papers and Proceedings, American Economic Association, vol. 114, pages 628-632, May.
    • Handle: RePEc:aea:apandp:v:114:y:2024:p:628-32
    DOI: 10.1257/pandp.20241073
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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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