Understanding and Predicting Recidivism in Latin America: A Machine Learning Approach

Recidivism is a persistent challenge for criminal justice systems worldwide, yet evidence from Latin America remains scarce. This study addresses that gap through three contributions. First, it reviews the individual, institutional, and contextual determinants of recidivism, with special attention to Latin America. Second, it examines the potential use of AI-based prediction tools, discussing the institutional, data-related, and ethical challenges such implementation entails. Third, using two decades of administrative data from Argentinas prison system, it applies six machine learning models to predict reoffending. The analysis identifies economic offenses and age at incarceration as the strongest predictors, while geographic indicators also play a role, reflecting the spatial clustering of repeat offenders across prisons. The findings suggest that routinely collected prison-level information, often underutilized, can enable reasonably accurate risk prediction and guide effective rehabilitation and prison management strategies.