AI and Nudging Framework for Recidivism Prevention and Sustainable Reintegration

This paper presents an AI-driven and nudging-based framework for recidivism prevention, integrating behavioral economics, decision theory, and non-cognitive skills theory. Traditional interventions treat re-offense as a moral failure and apply uniform supervision, often ignoring individual heterogeneity in impulsivity, risk-taking, and cognitive biases. Our model conceptualizes rehabilitation as the optimization of an individual's Economic Action Set (EAS), using AI for precise behavioral profiling and context-aware nudges. Simulation studies suggest this approach could theoretically reduce recidivism rates by 25–30%, support sustainable reintegration, and enhance societal productivity. The framework is scalable, cost-efficient, and preserves personal agency while providing dynamic lawful alternatives. Keywords: AI, Nudging, Recidivism Prevention, Economic Action Set, Behavioral Economics, Decision Theory, Non-Cognitive Skills, Rehabilitation, Social Reintegration License: CC BY 4.0