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.

翻译：审前风险评估工具被全国各地的司法管辖区用于评估“审前失败”的可能性，即被告未能出庭或再次犯罪的事件。司法官员随后利用这些评估来决定在审判期间是否释放或拘留被告。虽然算法风险评估工具旨在比法官更准确地预测审前失败，但仍有担忧认为，风险评估建议和审前决策都可能对少数群体存在偏见。在本文中，我们开发了研究方法以探究风险因素与审前失败之间的关联，同时根据被告种族估计审前风险评估和司法决策的误分类率。这一方法补充了日益增长的使用结果误分类方法来回答审前决策公平性问题的文献。我们为所提出的方法进行了详细的模拟研究，并将这些方法应用于弗吉尼亚州刑事司法服务部的数据。我们估计VPRAI算法具有接近完美的特异性，但其灵敏度因被告种族而异。司法决策也显示出偏见的证据；我们估计白人和黑人被告的错误拘留率分别为39.7%和51.4%。