  Pretrial risk assessment tools are used in jurisdictions across the country
to assess the likelihood of "pretrial failure," the event where defendants
either fail to appear for court or reoffend. Judicial officers, in turn, use
these assessments to determine whether to release or detain defendants during
trial. While algorithmic risk assessment tools were designed to predict
pretrial failure with greater accuracy relative to judges, there is still
concern that both risk assessment recommendations and pretrial decisions are
biased against minority groups. In this paper, we develop methods to
investigate the association between risk factors and pretrial failure, while
simultaneously estimating misclassification rates of pretrial risk assessments
and of judicial decisions as a function of defendant race. This approach adds
to a growing literature that makes use of outcome misclassification methods to
answer questions about fairness in pretrial decision-making. We give a detailed
simulation study for our proposed methodology and apply these methods to data
from the Virginia Department of Criminal Justice Services. We estimate that the
VPRAI algorithm has near-perfect specificity, but its sensitivity differs by
defendant race. Judicial decisions also display evidence of bias; we estimate
wrongful detention rates of 39.7% and 51.4% among white and Black defendants,
respectively.
