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Pretrial risk assessment tools are used in jurisdictions across the countryto assess the likelihood of "pretrial failure," the event where defendantseither fail to appear for court or reoffend. Judicial officers, in turn, usethese assessments to determine whether to release or detain defendants duringtrial. While algorithmic risk assessment tools were designed to predictpretrial failure with greater accuracy relative to judges, there is stillconcern that both risk assessment recommendations and pretrial decisions arebiased against minority groups. In this paper, we develop methods toinvestigate the association between risk factors and pretrial failure, whilesimultaneously estimating misclassification rates of pretrial risk assessmentsand of judicial decisions as a function of defendant race. This approach addsto a growing literature that makes use of outcome misclassification methods toanswer questions about fairness in pretrial decision-making. We give a detailedsimulation study for our proposed methodology and apply these methods to datafrom the Virginia Department of Criminal Justice Services. We estimate that theVPRAI algorithm has near-perfect specificity, but its sensitivity differs bydefendant race. Judicial decisions also display evidence of bias; we estimatewrongful detention rates of 39.7% and 51.4% among white and Black defendants,respectively.

An assessment of racial disparities in pretrial decision-making using misclassification models