Procedural Justice and Risk-Assessment Algorithms

31 Pages Posted: 22 Jun 2018 Last revised: 16 Jul 2018

A. J. Wang

Yale Law School

Date Written: June 21, 2018

Abstract

Statistical algorithms are increasingly used in the criminal justice system. Much of the recent scholarship on the use of these algorithms have focused on their "fairness," typically defined as accuracy across groups like race or gender. This project draws on the procedural justice literature to raise a separate concern: does the use of algorithms damage the perceived fairness and legitimacy of the criminal justice system? Through three original survey experiments on a nationally-representative sample, it shows that the public strongly disfavors algorithms as a matter of fairness, policy, and legitimacy. While respondents generally believe algorithms to be less accurate than either psychologists or statutory guidelines, accuracy alone does not explain their preferences. Creating "transparent" algorithms helps but is not enough to make algorithms desirable in their own right. Both surprising and troubling, members of the public seem more willing to tolerate disparate outcomes when they stem from an algorithm than a psychologist.

Keywords: algorithms, procedural justice, bail setting, statistics

Suggested Citation

Wang, A. J., Procedural Justice and Risk-Assessment Algorithms (June 21, 2018). Available at SSRN: https://ssrn.com/abstract=3170136 or http://dx.doi.org/10.2139/ssrn.3170136

A. J. Wang (Contact Author)

Yale Law School ( email )

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