Measuring Racial Discrimination in Algorithms

David Arnold, Will S. Dobbie and Peter Hull

No 28222, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: There is growing concern that the rise of algorithmic decision-making can lead to discrimination against legally protected groups, but measuring such algorithmic discrimination is often hampered by a fundamental selection challenge. We develop new quasi-experimental tools to overcome this challenge and measure algorithmic discrimination in the setting of pretrial bail decisions. We first show that the selection challenge reduces to the challenge of measuring four moments: the mean latent qualification of white and Black individuals and the race-specific covariance between qualification and the algorithm’s treatment recommendation. We then show how these four moments can be estimated by extrapolating quasi-experimental variation across as-good-as-randomly assigned decision-makers. Estimates from New York City show that a sophisticated machine learning algorithm discriminates against Black defendants, even though defendant race and ethnicity are not included in the training data. The algorithm recommends releasing white defendants before trial at an 8 percentage point (11 percent) higher rate than Black defendants with identical potential for pretrial misconduct, with this unwarranted disparity explaining 77 percent of the observed racial disparity in algorithmic recommendations. We find a similar level of algorithmic discrimination with regression-based recommendations, using a model inspired by a widely used pretrial risk assessment tool.

JEL-codes: C26 J15 K42 (search for similar items in EconPapers)
Date: 2020-12
New Economics Papers: this item is included in nep-big
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Published as David Arnold & Will Dobbie & Peter Hull, 2021. "Measuring Racial Discrimination in Algorithms," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 49-54, May.

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