Bail Reform and the (False) Racial Promise of Algorithmic Risk Assessment

95 Pages Posted: 13 Dec 2021 Last revised: 22 Jan 2023

See all articles by Sean Hill

Sean Hill

The Ohio State Moritz College of Law

Date Written: December 12, 2021

Abstract

Pretrial risk assessment instruments (PRAIs) have captured national attention in recent years. These instruments utilize computer algorithms to aid judges in making two predictions: whether a person will return to court while on pretrial release and whether a person will pose a danger to the public while on pretrial release. Assorted scholars, along with tool developers, contend that PRAIs are a reliable improvement upon the subjective assessments that judges and prosecutors currently make during pretrial proceedings. The risk scores generated by PRAIs will allegedly encourage judges to release people who have been classified as low-risk; monetary bail and pretrial detention will be reserved for the subset of individuals classified as medium- and high-risk. Because Black and Latinx people are over-represented in pretrial detention populations, and often have less disposable income than white defendants, they are routinely conceived as the beneficiaries of new bail schemes that rely on PRAIs.

This Article contends that PRAI advocates have been inattentive to how criminal law and policy sustains the subordination of nonwhite communities. This dominant framework is concerned with the disproportionate incarceration of Black and Latinx people but concludes that racial disparities emerge from an over-reliance on monetary bail and subjective risk assessment. Within the dominant framework, PRAIs will: evaluate risk according to objective criteria, identify exceptionally dangerous people for pretrial detention, and ensure monetary bail is set in an amount that facilitates the release of pretrial defendants.

The Article develops an anti-subordination framework to assess contemporary bail practices in relation to PRAIs. This framework emphasizes the unreliability of dangerousness predictions and how these predictions contributed to the dramatic growth in detention populations following the Civil Rights Movement. PRAIs do not resolve the inaccuracies that attend these predictions, but instead derive their risk scores from criminal data that reflects the biased and subjective predictions of the past. Rather than facilitate racial equality, the anti-subordination framework concludes that PRAIs legitimize dangerousness predictions and usher in bail regimes that continue to disproportionately harm Black and Latinx people.

The Article directs attention to two particular states—California and New York—in which grassroots coalitions launched prominent bail campaigns, seeking legislation that would dramatically reduce pretrial detention populations and rectify racial disparities within those populations. Application of the anti-subordination framework uncovers how PRAIs influenced the success of the respective campaigns, and how the instruments can function to neutralize the decarceral demands made by social movements.

Keywords: Bail, Bail Reform, Criminal Law, Criminal Procedure, Critical Theory, Critical Race Theory, Social Movements

Suggested Citation

Hill, Sean, Bail Reform and the (False) Racial Promise of Algorithmic Risk Assessment (December 12, 2021). 68 UCLA L. Rev. 910 (2021-2022) , Ohio State Legal Studies Research Paper No. 674, Available at SSRN: https://ssrn.com/abstract=3683790

Sean Hill (Contact Author)

The Ohio State Moritz College of Law ( email )

55 West 12th Avenue
Columbus, OH 43210
United States

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