Research handbook on Big Data Law
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. Most instruments are relatively simple tools that assign the individual to a risk category representing the probability of recidivism if not detained. Some algorithms aim to provide information not only about risk assessment but also about risk management, or the type of intervention that might most effectively reduce risk. These risk assessment instruments (RAIs) might be used at a number of points in the criminal process. 1 They may be used 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. As a general matter, judges, parole boards, and correctional officials have discretion as to how much weight to give the outputs of such instruments. Prior to the advent of RAIs, legal decision-makers called upon to evaluate an offender’s risk usually relied on the opinions of mental health professionals, probation officer assessments, or their own seat-of-the pants analysis. This type of judgment is often called “clinical” prediction—to distinguish it from “actuarial,” statistically based prediction—and it is still the basis of the post-conviction and pretrial decision-making processes in many jurisdictions. 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.
Goel, Sharad, Ravi Shroff, Jennifer Skeem, and Christopher Slobogin. "The accuracy, equity, and jurisprudence of criminal risk assessment." Research handbook on Big Data Law. Ed. Roland Vogl. Edward Elgar Publishing, 2021, 9-28.