Abstract

There remain serious concerns about the potential for pretrial risk assessments to exacerbate racial disparities. Yet, current evidence on differential prediction in pretrial risk assessments is limited. The present investigation tests for differential prediction by race as an indication of bias in Indiana Risk Assessment System–Pretrial Assessment Tool (IRAS-PAT) assessments. Using pooled data drawn from a five-county IRAS-PAT validation, which included 689 Black and 2,850 White defendants, we primarily used a hierarchical regression-based approach to test between-group differences in the slopes of regression lines. Where slope-based bias was present, differential prediction was reevaluated once algorithmic corrections were applied. Findings showed IRAS-PAT assessments produced less accurate predictions of pretrial misconduct for Black defendants relative to White defendants. Only one debiasing strategy—which accounted for item-level differences across subgroups—corrected differential prediction. Debiasing strategies can mitigate differential prediction but may have limited utility for local jurisdictions under current legal frameworks.

摘要

人们仍然严重担心审前风险评估可能会加剧种族差异。然而，目前关于审前风险评估中差异预测的证据有限。目前的调查测试了按种族进行的差异预测，作为印第安纳州风险评估系统-审前评估工具（IRAS-PAT）评估中偏差的指示。使用从五个县的 IRAS-PAT 验证中提取的汇总数据（其中包括 689 名黑人被告和 2,850 名白人被告），我们主要使用基于分层回归的方法来测试回归线斜率的组间差异。在存在基于斜率的偏差的情况下，一旦应用算法校正，就会重新评估差异预测。调查结果显示，相对于白人被告，IRAS-PAT 评估对黑人被告审前不当行为的预测不太准确。只有一种去偏策略（解释了子组之间项目级别的差异）纠正了差异预测。去偏策略可以减轻差异预测，但在当前法律框架下对当地司法管辖区的效用可能有限。