Dr. Ravi Shroff and colleagues published a chapter in the Research Handbook on Big Data Law that critically examines the use of statistical algorithms that help predict if people will be repeat offenders.
Jurisdictions across the country, including the federal government through its recently enacted First Step Act, have begun using statistical algorithms (also called “instruments”) to help determine an arrestee’s or an offender’s risk of reoffending. These risk assessment instruments (RAIs) might be used at a number of points in the criminal process, including at the front-end by judges to impose a sentence after conviction, at the back-end by parole boards to make decisions about prison release, or in between these two points by correctional authorities determining the optimal security and service arrangements for an offender. At the pretrial stage, RAIs might come into play at the time of the bail or pretrial detention determination by a judge, which usually takes place shortly after arrest. The increased use of RAIs in the criminal justice system has given rise to several criticisms. RAIs are said to be no more accurate than clinical assessments, racially biased, lacking in transparency and, because of their quantitative nature, dehumanizing. This chapter critically examines a number of these concerns. It also highlights how the law has, and should, respond to these issues.