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Risk Assessment: Machine Learning vs. Regression—Which Direction Should We Go
Fri, Nov 18, 9:30 to 10:50am, Hilton, Marlborough B, 2nd LevelSession Submission Type: Complete Thematic Panel
Abstract
The Division on Corrections & Sentencing's first handbook is on a very important topic in our field—risk and need assessment. Several papers explored the use of machine learning as a methodology to advance both the science of risk assessment and the art of using the information. This panel will examine the issues, discuss the pros and cons, and explore how to develop the methodology of risk and need assessment.
Sub Unit
Individual Presentations
Isolating Modeling Effects in Offender Risk Assessment - Zachary Hamilton, Washington State University; Melanie-Angela Neuilly, Washington State University; Stephen Lee, University of Idaho
Taking the Long View of Risk Assessment: The Promise and Pitfalls of Machine Learning in Actuarial Decision-Making for Criminal Justice Agencies - KiDeuk Kim, Urban Institute; Grant Duwe, Minnesota Department of Corrections
Using Predictive Analytics and Machine Learning to Improve the Accuracy and Performance of Juvenile Justice Risk Assessment Instruments: The Florida Case Study - Ira Schwartz, Consultant and Advisor to Algorhythm