Discrimination in the Age of Algorithms

45 Pages Posted: 7 Feb 2019 Last revised: 12 Feb 2019

See all articles by Jon Kleinberg

Jon Kleinberg

Cornell University - Department of Computer Science

Jens Ludwig

Georgetown University - Public Policy Institute (GPPI); National Bureau of Economic Research (NBER); IZA Institute of Labor Economics

Sendhil Mullainathan

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

Cass R. Sunstein

Harvard Law School; Harvard University - Harvard Kennedy School (HKS)

Multiple version iconThere are 2 versions of this paper

Date Written: February 5, 2019

Abstract

The law forbids discrimination. But the ambiguity of human decision-making often makes it extraordinarily hard for the legal system to know whether anyone has actually discriminated. To understand how algorithms affect discrimination, we must therefore also understand how they affect the problem of detecting discrimination. By one measure, algorithms are fundamentally opaque, not just cognitively but even mathematically.  Yet for the task of proving discrimination, processes involving algorithms can provide crucial forms of transparency that are otherwise unavailable. These benefits do not happen automatically. But with appropriate requirements in place, the use of algorithms will make it possible to more easily examine and interrogate the entire decision process, thereby making it far easier to know whether discrimination has occurred.  By forcing a new level of specificity, the use of algorithms also highlights, and makes transparent, central tradeoffs among competing values. Algorithms are not only a threat to be regulated; with the right safeguards in place, they have the potential to be a positive force for equity.

Suggested Citation

Kleinberg, Jon and Ludwig, Jens and Mullainathan, Sendhil and Sunstein, Cass R., Discrimination in the Age of Algorithms (February 5, 2019). Available at SSRN: https://ssrn.com/abstract=3329669 or http://dx.doi.org/10.2139/ssrn.3329669

Jon Kleinberg

Cornell University - Department of Computer Science ( email )

4130 Upson Hall
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Jens Ludwig

Georgetown University - Public Policy Institute (GPPI) ( email )

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National Bureau of Economic Research (NBER)

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Sendhil Mullainathan

University of Chicago ( email )

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National Bureau of Economic Research (NBER) ( email )

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Cass R. Sunstein (Contact Author)

Harvard Law School ( email )

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Harvard University - Harvard Kennedy School (HKS) ( email )

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