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Preventative Justice: How Algorithms, Parole Boards and Limiting Retributivism Could End Mass Incarceration

dc.contributor.authorSlobogin, Christopher
dc.identifier.citation56 Wake Forest L. Rev. 97 (2021)en_US
dc.descriptionarticle published in a law reviewen_US
dc.description.abstractA number of states use statistically derived algorithms to provide estimates of the risk of reoffending. In theory, these risk assessment instruments could bring significant benefits. Fewer people of all ethnicities would be put in jail prior to trial and in prison after conviction, the duration of sentences would be reduced for low-risk offenders, and treatment resources would be more efficiently allocated. As a result, the capital outlays for prisons and jails would be substantially reduced. The public would continue to be protected from the most dangerous individuals, while lower-risk individuals would be less subject to the criminogenic effects of incarceration and better positioned to build and maintain a life outside of jail or prison that does not involve criminal activity. Risk assessment instruments cannot fully realize these benefits, however, unless the currently popular determinate sentencing structure that exists in most states is dramatically altered. Today, determinate sentencing states give almost all sentencing power to prosecutors, who in essence fix the sentence range through charging practices, and judges, who decide where within the range the sentence will fall and occasionally select a sentence outside that range. The result is that even an offender who poses a low risk of reoffending will often receive a lengthy sentence of imprisonment.en_US
dc.publisherWake Forest Law Reviewen_US
dc.subjectrisk assessment, incarceration, determinate sentencingen_US
dc.titlePreventative Justice: How Algorithms, Parole Boards and Limiting Retributivism Could End Mass Incarcerationen_US

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