刑事司法算法中的不确定性:宾夕法尼亚加性分类工具的模拟研究(CS)

很多人关注的是与量刑、保释、假释决定和再犯相关的算法，而较少人关注的是carceral算法，这些算法被用来确定一个囚犯的生活经历。在本文中，我们研究了一种这样的算法，宾夕法尼亚加性分类工具(PACT)，它为被监禁的个人分配拘留级别。我们分析PACT的方式与刑事司法算法经常被分析的方式一样:即，我们为PACT训练一个精确的机器学习模型;我们研究它在性别、年龄和种族方面的公平性;我们确定哪些特征是最重要的。除了这些传统的计算方法外，我们提出并实施了一些新的方法来研究这些算法。与其关注结果本身，我们建议将我们的注意力转移到结果的可变性上，特别是因为许多carceral算法被反复使用，可能会有不确定性的传播。通过执行几个分配托管级别的模拟，我们揭示了像PACT这样的工具的问题方面。

原文题目：Uncertainty in Criminal Justice Algorithms: simulation studies of the Pennsylvania Additive Classification Tool

原文：Much attention has been paid to algorithms related to sentencing, the setting of bail, parole decisions and recidivism while less attention has been paid to carceral algorithms, those algorithms used to determine an incarcerated individual's lived experience. In this paper we study one such algorithm, the Pennsylvania Additive Classification Tool (PACT) that assigns custody levels to incarcerated individuals. We analyze the PACT in ways that criminal justice algorithms are often analyzed: namely, we train an accurate machine learning model for the PACT; we study its fairness across sex, age and race; and we determine which features are most important. In addition to these conventional computations, we propose and carry out some new ways to study such algorithms. Instead of focusing on the outcomes themselves, we propose shifting our attention to the variability in the outcomes, especially because many carceral algorithms are used repeatedly and there can be a propagation of uncertainty. By carrying out several simulations of assigning custody levels, we shine light on problematic aspects of tools like the PACT.