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Full-Text Articles in Law
Bias In, Bias Out, Sandra G. Mason
Bias In, Bias Out, Sandra G. Mason
AI-DR Collection
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 …
Constructing Recidivism Risk, Jessica Eaglin
Constructing Recidivism Risk, Jessica Eaglin
AI-DR Collection
Courts increasingly use actuarial—meaning statistically derived—information about a defendant’s likelihood of engaging in criminal behavior in the future at sentencing. This Article examines how developers construct the tools that predict recidivism risk. It exposes the numerous choices that developers make during tool construction with serious consequences to sentencing law and policy. These design decisions require normative judgments concerning accuracy, equality, and the purpose of punishment. Whether and how to address these concerns reflects societal values about the administration of criminal justice more broadly. Currently, developers make these choices in the absence of law, even as they face distinct interests that …