利用最优运输和共形预测集提高刑事司法风险评估的公平性
Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Optimal Transport and Conformal Prediction Sets
摘要：
在美国和其他地方，风险评估算法正被用于帮助刑事司法决策者了解情况。一个共同的意图是预测罪犯的"未来犯罪。这些算法因潜在的不公平而受到了正确的批评，有一个活跃的家庭手工业试图进行修复。在本文中，我们使用反事实推理来考虑改善公平性的前景时，一个特权较低的群体的成员被视为一个风险算法，如果他们是一个更特权的群体的成员。我们将以一种新的方式训练的机器学习分类器与相关联合概率分布的最佳运输调整相结合，它们共同提供了对偏差输入偏差输出的建设性响应。一个关键的区别是公平主张之间的经验检验和公平主张是不是。然后，我们使用混淆表和共形预测集来评估预测风险的公平性。我们的数据是对美国一个大都市地区的30万名罪犯进行传讯时的随机样本，在此期间，他们会做出释放或拘留的决定。我们表明，在公平性的实质性改善，可以实现一致的帕累托改善受保护的群体。
英文摘要：
In the United States and elsewhere, risk assessment algorithms are being used to help inform criminal justice decision-makers. A common intent is to forecast an offender's ``future dangerousness.'' Such algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs. In this paper, we use counterfactual reasoning to consider the prospects for improved fairness when members of a less privileged group are treated by a risk algorithm as if they are members of a more privileged group. We combine a machine learning classifier trained in a novel manner with an optimal transport adjustment for the relevant joint probability distributions, which together provide a constructive response to claims of bias-in-bias-out. A key distinction is between fairness claims that are empirically testable and fairness claims that are not. We then use confusion tables and conformal prediction sets to evaluate achieved fairness for projected risk. Our data are a random sample of 300,000 offenders at their arraignments for a large metropolitan area in the United States during which decisions to release or detain are made. We show that substantial improvement in fairness can be achieved consistent with a Pareto improvement for protected groups.
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