Scoping review on the integration of artificial intelligence and machine learning with structured instruments to predict juvenile recidivism

Resumen

Predicting the risk of recidivism in juvenile justice has traditionally relied on structured instruments, while the incorporation of AI/ML models has been proposed as a means of improving their performance. However, the actual added value of these models, their integration with structured tools, and their implications for practice remain a subject of debate. This scoping review evaluates whether integrating AI/ML models with structured risk assessment tools in juvenile justice meaningfully enhances recidivism prediction and practice. Five studies that met these criteria were included. Overall, the results show a consistent pattern of moderate discrimination and generally marginal improvements as model complexity increases, suggesting the existence of a certain empirical limit to predictive capacity in this field. Improvements are more likely when the comparator is strictly linear or when contrasted with unstructured judgment, suggesting that the added value of AI/ML may lie in modeling interactions between variables rather than in a substantial increase in accuracy. Nevertheless, aggregate performance may mask relevant differences among subgroups, including disparities associated with sociodemographic variables, which raises implications regarding equity and institutional legitimacy. Added to this are the challenges arising from their implementation, including model traceability, the interaction between professionals and these automated systems, and the need for frameworks that ensure oversight and auditing.