An Economic Approach to Regulating Algorithms

49 Pages Posted: 12 May 2020 Last revised: 29 Jan 2021

See all articles by Ashesh Rambachan

Ashesh Rambachan

Harvard University

Jon Kleinberg

Cornell University - Department of Computer Science

Sendhil Mullainathan

University of Chicago

Jens Ludwig

University of Chicago; National Bureau of Economic Research (NBER)

Date Written: May 2020

Abstract

There is growing concern about "algorithmic bias" - that predictive algorithms used in decision-making might bake in or exacerbate discrimination in society. We argue that such concerns are naturally addressed using the tools of welfare economics. This approach overturns prevailing wisdom about the remedies for algorithmic bias. First, when a social planner builds the algorithm herself, her equity preference has no effect on the training procedure. So long as the data, however biased, contain signal, they will be used and the learning algorithm will be the same. Equity preferences alone provide no reason to alter how information is extracted from data - only how that information enters decision-making. Second, when private (possibly discriminatory) actors are the ones building algorithms, optimal regulation involves algorithmic disclosure but otherwise no restriction on training procedures. Under such disclosure, the use of algorithms strictly reduces the extent of discrimination relative to a world in which humans make all the decisions.

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Suggested Citation

Rambachan, Ashesh and Kleinberg, Jon and Mullainathan, Sendhil and Ludwig, Jens, An Economic Approach to Regulating Algorithms (May 2020). NBER Working Paper No. w27111, Available at SSRN: https://ssrn.com/abstract=3597843

Ashesh Rambachan (Contact Author)

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Jon Kleinberg

Cornell University - Department of Computer Science ( email )

4130 Upson Hall
Ithaca, NY 14853-7501
United States

Sendhil Mullainathan

University of Chicago ( email )

1101 East 58th Street
Chicago, IL 60637
United States

Jens Ludwig

University of Chicago ( email )

1101 East 58th Street
Chicago, IL 60637
United States

National Bureau of Economic Research (NBER) ( email )

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Cambridge, MA 02138
United States

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