Although risk assessment has increasingly been used as a tool to help reform the criminal justice system, some stakeholders are adamantly opposed to using algorithms. The principal concern is that any benefits achieved by safely reducing rates of incarceration will be offset by costs to racial justice claimed to be inherent in the algorithms themselves. But fairness trade‐offs are inherent to the task of predicting recidivism, whether the prediction is made by an algorithm or human. Based on a matched sample of 67,784 Black and White federal supervisees assessed with the Post Conviction Risk Assessment, we compared how three alternative strategies for “debiasing” algorithms affect these trade‐offs, using arrest for a violent crime as the criterion . These candidate algorithms all strongly predict violent reoffending (areas under the curve = 0.71–72), but vary in their association with race (r = 0.00–0.21) and shift trade‐offs between balance in positive predictive value and false‐positive rates. Providing algorithms with access to race (rather than omitting race or “blinding” its effects) can maximize calibration and minimize imbalanced error rates. Implications for policymakers with value preferences for efficiency versus equity are discussed.

中文翻译：

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使用算法来解决预测累犯所固有的折衷。

尽管风险评估已越来越多地用作帮助改革刑事司法系统的工具，但一些利益相关者坚决反对使用算法。主要关注的是，通过安全降低监禁率所获得的任何利益都将被算法本身固有的种族正义成本所抵消。但是，无论是通过算法还是人工进行预测，公平性折衷对于预测累犯都是固有的。基于通过定罪后风险评估评估的67,784名黑人和白人联邦监管人员的匹配样本，我们以暴力犯罪的逮捕为标准，比较了三种“消除偏见”算法的替代策略如何影响这些取舍。这些候选算法都强烈预测暴力重犯（曲线下的区域= 0。r = 0.00-0.21），并在正预测值余额与假阳性率之间进行权衡取舍。为算法提供访问种族的机会（而不是忽略种族或使其影响“蒙蔽”）可以最大化校准并最大程度地减少失衡的错误率。讨论了对价值偏好效率与公平的政策制定者的启示。