Social Determinants of Recidivism: A Machine Learning Solution

Current literature in criminal justice analytics often focuses on predicting the likelihood of recidivism (repeat offenses committed by released defendants), but this problem is fraught with ethical missteps ranging from selection bias in data collection to model interpretability. This paper re-purposes Machine Learning (ML) in criminal justice to identify social determinants of recidivism, with contributions along three dimensions. (1) We shift the focus from predicting which individuals will re-offend to identifying the broader underlying factors that explain differences in recidivism, with the goal of providing a reliable framework for preventative policy intervention. (2) Recidivism models typically agglomerate all individuals into one dataset to carry out ML tasks. We instead apply unsupervised learning to reduce noise and extract homogeneous subgroups of individuals, with a novel heuristic to find the optimal number of subgroups. (3) We subsequently apply supervised learning within the subgroups to determine statistically significant features that are correlated to recidivism. It is our view that this new approach to a long-standing question will serve as a useful guide for the practical application of ML in policymaking.