Evidence-Based Sentencing and the Scientific Rationalization of Discrimination

51 Pages Posted: 1 Sep 2013 Last revised: 10 Sep 2013

Sonja B. Starr

University of Michigan Law School

Date Written: September 1, 2013


This paper critiques, on legal and empirical grounds, the growing trend of basing criminal sentences on actuarial recidivism risk prediction instruments that include demographic and socioeconomic variables. I argue that this practice violates the Equal Protection Clause and is bad policy: an explicit embrace of otherwise-condemned discrimination, sanitized by scientific language. To demonstrate that this practice should be subject to heightened constitutional scrutiny, I comprehensively review the relevant case law, much of which has been ignored by existing literature. To demonstrate that it cannot survive that scrutiny and is undesirable policy, I review the empirical evidence underlying the instruments. I show that they provide wildly imprecise individual risk predictions, that there is no compelling evidence that they outperform judges’ informal predictions, that less discriminatory alternatives would likely perform as well, and that the instruments do not even address the right question: the effect of a given sentencing decision on recidivism risk. Finally, I also present new, suggestive empirical evidence, based on a randomized experiment using fictional cases, that these instruments should not be expected merely to substitute actuarial predictions for less scientific risk assessments, but instead to increase the weight given to recidivism risk versus other sentencing considerations.

Suggested Citation

Starr, Sonja B., Evidence-Based Sentencing and the Scientific Rationalization of Discrimination (September 1, 2013). Stanford Law Review, Forthcoming; U of Michigan Law & Econ Research Paper No. 13-014. Available at SSRN: https://ssrn.com/abstract=2318940

Sonja B. Starr (Contact Author)

University of Michigan Law School ( email )

625 South State St
Ann Arbor, MI 48109
United States
617 821-1222 (Phone)

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