Prison data analysis finds choosing lowest reoffending risk scores preserves overall accuracy
Whether an individual reoffends after their release from prison or is successfully rehabilitated depends on several factors. When assessing the risk of recidivism, all of these are taken into account—both by human experts and by machine learning models. If several equally reliable computational mode...
Image: Phys.org
Whether an individual reoffends after their release from prison or is successfully rehabilitated depends on several factors. When assessing the risk of recidivism, all of these are taken into account—both by human experts and by machine learning models. If several equally reliable computational models produce different risk ratings, the lowest value should be used for the individual concerned, suggest Ashwin Singh (TU Wien) and Carlos Castillo (ICREA/Universitat Pompeu Fabra).
Originally published at Phys.org
