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Prison data analysis finds choosing lowest reoffending risk scores preserves overall accuracy

Prison data analysis finds choosing lowest reoffending risk scores preserves overall accuracy
Phys.org Tuesday 10 November 2026, 23:00 UTC 1 min read
Key Points

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).

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).
Prison (ORG) Ashwin Singh (PERSON) Wien (PERSON) Carlos Castillo (PERSON) ICREA/Universitat Pompeu Fabra (ORG)
Originally published by Phys.org Read original →

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