In this study, we propose advancements in criminal justice analytics along three dimensions. First, for the long-standing problem of recidivism risk assessment, we shift the focus from predicting the likelihood of recidivism to identifying its underlying determinants within distinct subgroups. Second, to achieve this, we introduce a machine learning pipeline that combines unsupervised and supervised techniques to identify homogeneous clusters of individuals and find statistically significant determinants of recidivism within each cluster. We demonstrate useful heuristics to address key challenges in this pipeline related to parameter selection and data processing. Third, we use these results to compare outcomes across subgroups, enabling a more nuanced understanding of the root factors that lead to differences in recidivism. Overall, this approach aims to explore new ways of addressing long-standing criminal justice challenges, providing a reliable framework for informed policy intervention.

中文翻译：

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累犯的社会决定因素：机器学习解决方案

在这项研究中，我们从三个方面提出了刑事司法分析方面的进展。首先，对于长期存在的累犯风险评估问题，我们将重点从预测累犯的可能性转移到确定其在不同亚组中的潜在决定因素。其次，为了实现这一目标，我们引入了一种机器学习管道，该管道将无监督和受监督的技术相结合，以识别个体的同质集群，并在每个集群内找到统计意义上的累犯决定因素。我们展示了有用的启发式方法来解决此管道中与参数选择和数据处理有关的关键挑战。第三，我们使用这些结果比较各个亚组的结果，从而更细致地理解导致累犯差异的根本因素。总体，