School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy.
Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy.
Int J Environ Res Public Health. 2022 Aug 25;19(17):10594. doi: 10.3390/ijerph191710594.
Recent evolution in the field of data science has revealed the potential utility of machine learning (ML) applied to criminal justice. Hence, the literature focused on finding better techniques to predict criminal recidivism risk is rapidly flourishing. However, it is difficult to make a state of the art for the application of ML in recidivism prediction. In this systematic review, out of 79 studies from Scopus and PubMed online databases we selected, 12 studies that guarantee the replicability of the models across different datasets and their applicability to recidivism prediction. The different datasets and ML techniques used in each of the 12 studies have been compared using the two selected metrics. This study shows how each method applied achieves good performance, with an average score of 0.81 for ACC and 0.74 for AUC. This systematic review highlights key points that could allow criminal justice professionals to routinely exploit predictions of recidivism risk based on ML techniques. These include the presence of performance metrics, the use of transparent algorithms or explainable artificial intelligence (XAI) techniques, as well as the high quality of input data.
近年来，数据科学领域的发展揭示了机器学习（ML）在刑事司法中的潜在应用价值。因此，专注于寻找更好的技术来预测犯罪再犯风险的文献迅速涌现。然而，要了解 ML 在再犯预测中的应用现状却很困难。在这项系统评价中，我们从 Scopus 和 PubMed 在线数据库中选择了 79 项研究，其中有 12 项研究保证了模型在不同数据集上的可重复性及其在再犯预测中的适用性。我们使用这两个选定的指标比较了这 12 项研究中每个研究使用的不同数据集和 ML 技术。这项研究展示了每种方法是如何实现良好的性能的，ACC 的平均得分为 0.81，AUC 的平均得分为 0.74。这项系统评价强调了一些关键点，这些关键点可以使刑事司法专业人员能够根据 ML 技术常规地利用再犯风险预测。这些关键点包括性能指标的存在、透明算法或可解释人工智能（XAI）技术的使用，以及高质量的输入数据。
