Algorithmic Risk Assessment in the Hands of Humans

62 Pages Posted: 5 Dec 2019 Last revised: 8 Nov 2022

See all articles by Megan T. Stevenson

Megan T. Stevenson

University of Virginia School of Law

Jennifer L. Doleac

Texas A&M University - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: September 29, 2022

Abstract

We evaluate the impacts of adopting algorithmic risk assessments in sentencing. We find that judges changed sentencing practices in response to the risk assessment, but that discretion played a large role in mediating its impact. Judges deviated from the recommendations associated with the algorithm in systematic ways, suggestive of alternative objectives at sentencing. As a result, risk assessment did not lead to detectable gains in terms of public safety or reduced incarceration rates. Using simulations, we show that strict adherence to the sentencing recommendations associated with the algorithm would have led to some benefits (a sharp reduction in incarceration) but also some costs (a slight increase in recidivism and an increase in relative sentences for the young). Discretion mitigated the costs at the expense of reducing the benefits.

Keywords: risk assessment, algorithms, sentencing

JEL Classification: K14

Suggested Citation

Stevenson, Megan and Doleac, Jennifer L., Algorithmic Risk Assessment in the Hands of Humans (September 29, 2022). Available at SSRN: https://ssrn.com/abstract=3489440 or http://dx.doi.org/10.2139/ssrn.3489440

Megan Stevenson (Contact Author)

University of Virginia School of Law ( email )

Jennifer L. Doleac

Texas A&M University - Department of Economics ( email )

5201 University Blvd.
College Station, TX 77843-4228
United States

HOME PAGE: http://jenniferdoleac.com/

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
2,540
Abstract Views
16,208
Rank
7,654
PlumX Metrics