In God We Trust. All Others Must Bring Data

Session Submission Type: Complete Thematic Panel

Abstract

The potential of machine learning is increasingly recognized in numerous fields. Criminal justice is no exception. Machine learning has been applied to risk assessments that predict risk of recidivism and other criminal justice outcomes for individuals involved in the criminal justice system. This panel features innovative research on the state-of-the-art risk and needs assessments, using machine learning algorithms. Panelists will present the latest developments in machine learning applications for risk and needs assessments and new frameworks for thinking about how to inform criminal justice decision-making.

Sub Unit

• Area VII. Criminal Justice Policy and Practice / 32. Considering Criminal Justice Policies

Individual Presentations

• The SAFER Pretrial Assessment Tool - Zachary Hamilton, Washington State University; Mia Jane Abboud, Washington State University; Alex Keigerl, Washington State University; Jacqueline van Wormer, Washington State University

• Using Machine Learning to construct an Internal Classification of Male Prison Inmates - Tim Brennan, Northpointe Institute; William Dieterich, Equivant; Christina Mendoza, Equivant

• Simulated Recidivism Risk Assessment: A New Way to Assess Criminogenic Needs? - Grant Duwe, Minnesota Department of Corrections; KiDeuk Kim, Urban Institute

Chair

• KiDeuk Kim, Urban Institute