An Economic Perspective on Algorithmic Fairness

Ashesh Rambachan, Jon Kleinberg, Jens Ludwig and Sendhil Mullainathan

AEA Papers and Proceedings, 2020, vol. 110, 91-95

Abstract: There are widespread concerns that the growing use of machine learning algorithms in important decisions may reproduce and reinforce existing discrimination against legally protected groups. Most of the attention to date on issues of "algorithmic bias" or "algorithmic fairness" has come from computer scientists and machine learning researchers. We argue that concerns about algorithmic fairness are at least as much about questions of how discrimination manifests itself in data, decision-making under uncertainty, and optimal regulation. To fully answer these questions, an economic framework is necessary—and as a result, economists have much to contribute.

JEL-codes: C45 D63 D81 J15 J16 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (9)

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DOI: 10.1257/pandp.20201036

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