Governing by Algorithm? No Noise and (Potentially) Less Bias

17 Pages Posted: 23 Sep 2021 Last revised: 28 Oct 2021

See all articles by Cass R. Sunstein

Cass R. Sunstein

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

Date Written: September 15, 2021

Abstract

As intuitive statisticians, human beings suffer from identifiable biases, cognitive and otherwise. Human beings can also be “noisy,” in the sense that their judgments show unwanted variability. As a result, public institutions, including those that consist of administrative prosecutors and adjudicators, can be biased, noisy, or both. Both bias and noise produce errors. Algorithms eliminate noise, and that is important; to the extent that they do so, they prevent unequal treatment and reduce errors. In addition, algorithms do not use mental short-cuts; they rely on statistical predictors, which means that they can counteract or even eliminate cognitive biases. At the same time, the use of algorithms, by administrative agencies, raises many legitimate questions and doubts. Among other things, they can encode or perpetuate discrimination, perhaps because their inputs are based on discrimination, perhaps because what they are asked to predict is infected by discrimination. But if the goal is to eliminate discrimination, properly constructed algorithms nonetheless have a great deal of promise for administrative agencies.

Keywords: Bias, noise, algorithms, bail

JEL Classification: D90, D91, D80, D81

Suggested Citation

Sunstein, Cass R., Governing by Algorithm? No Noise and (Potentially) Less Bias (September 15, 2021). Harvard Public Law Working Paper No. 21-35, Available at SSRN: https://ssrn.com/abstract=3925240 or http://dx.doi.org/10.2139/ssrn.3925240

Cass R. Sunstein (Contact Author)

Harvard Law School ( email )

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

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