Law Commons^™

Dividing Bail Reform, Shima Baughman

Utah Law Faculty Scholarship

There are few issues in criminal law with greater momentum than bail reform. In the last three years, states have passed hundreds of new pretrial release laws, and there are now over 200 bills pending throughout the states. These efforts are rooted in important concerns: Bail reform lies at the heart of broader recent debates about equitable treatment in the criminal justice system. Done right, bail keeps dangerous individuals off the streets; done wrong, it keeps those with less economic means in jail longer. Some jurisdictions are eliminating money bail. Others are adopting risk assessments to determine who to release. …

Algorithmic Risk Assessments And The Double-Edged Sword Of Youth, Megan T. Stevenson, Christopher Slobogin

Christopher Slobogin

Risk assessment algorithms—statistical formulas that predict the likelihood a person will commit crime in the future—are used across the country to help make life-altering decisions in the criminal process, including setting bail, determining sentences, selecting probation conditions, and deciding parole. Yet many of these instruments are “black-box” tools. The algorithms they use are secret, both to the sentencing authorities who rely on them and to the offender whose life is affected. The opaque nature of these tools raises numerous legal and ethical concerns. In this paper we argue that risk assessment algorithms obfuscate how certain factors, usually considered mitigating by …

Bias In, Bias Out, Sandra G. Mayson

Scholarly Works

Police, prosecutors, judges, and other criminal justice actors increasingly use algorithmic risk assessment to estimate the likelihood that a person will commit future crime. As many scholars have noted, these algorithms tend to have disparate racial impact. In response, critics advocate three strategies of resistance: (1) the exclusion of input factors that correlate closely with race, (2) adjustments to algorithmic design to equalize predictions across racial lines, and (3) rejection of algorithmic methods altogether.

This Article’s central claim is that these strategies are at best superficial and at worst counterproductive, because the source of racial inequality in risk assessment lies …