An Economic Perspective on Algorithmic Fairness
AEA Papers and Proceedings
vol. 110,
May 2020
(pp. 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.Citation
Rambachan, Ashesh, Jon Kleinberg, Jens Ludwig, and Sendhil Mullainathan. 2020. "An Economic Perspective on Algorithmic Fairness." AEA Papers and Proceedings, 110: 91-95. DOI: 10.1257/pandp.20201036Additional Materials
JEL Classification
- C45 Neural Networks and Related Topics
- D63 Equity, Justice, Inequality, and Other Normative Criteria and Measurement
- D81 Criteria for Decision-Making under Risk and Uncertainty
- J15 Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
- J16 Economics of Gender; Non-labor Discrimination