Machine Learning for Early Mental Health Support and Offenders Correction

Abstract

The effective rehabilitation and supervision of law offenders are vital to promoting community safety and enabling individuals to reintegrate into society. Community supervision presents several challenges for agencies like the Adult Parole Authority (APA), which must oversee individuals released from prisons under various forms of supervision, including courtesy supervision for different counties and interstate compact cases. With such a large number of individuals under supervision, the APA struggles to provide adequate oversight and support to guide individuals towards positive behavioral changes and reduce the risk of recidivism. To address these challenges, this paper proposes a machine learning-based system designed to monitor and support individuals under community supervision. The model would track various indicators to identify individuals at risk of self-harm or harming others and enable the APA to provide timely and appropriate support to these individuals. Improving the monitoring and support offered during the rehabilitation and supervision period would enhance the effectiveness of community supervision and contribute to safer and more stable communities.