Law Commons^™

Bias In, Bias Out, Sandra G. Mayson

All Faculty Scholarship

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 impacts. 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 …

Dangerous Defendants, Sandra G. Mayson

All Faculty Scholarship

Bail reform is gaining momentum nationwide. Reformers aspire to untether pretrial detention from wealth (the ability to post money bail) and condition it instead on statistical risk, particularly the risk that a defendant will commit crime if he remains at liberty pending trial. The bail reform movement holds tremendous promise, but also forces the criminal justice system to confront a difficult question: What statistical risk that a person will commit future crime justifies short-term detention? What about lesser restraints, like GPS monitoring? Although the turn to actuarial risk assessment in the pretrial context has engendered both excitement and concern, the …