NDLI: Forecasts of Violence to Inform Sentencing Decisions

Content Provider	Springer Nature Link
Author	Berk, Richard Bleich, Justin
Copyright Year	2013
Abstract	Recent legislation in Pennsylvania mandates that forecasts of "future dangerousness" be provided to judges when sentences are given. Similar requirements already exist in other jurisdictions. Research has shown that machine learning can lead to usefully accurate forecasts of criminal behavior in such setting. But there are settings in which there is insufficient IT infrastructure to support machine learning. The intent of this paper is provide a prototype procedure for making forecasts of future dangerousness that could be used to inform sentencing decisions when machine learning is not practical. We consider how classification trees can be improved so that they may provide an acceptable second choice.We apply an version of classifications trees available in R, with some technical enhancements to improve tree stability. Our approach is illustrated with real data that could be used to inform sentencing decisions.Modest sized trees grown from large samples can forecast well and in a stable fashion, especially if the small fraction of indecisive classifications are found and accounted for in a systematic manner. But machine learning is still to be preferred when practical.Our enhanced version of classifications trees may well provide a viable alternative to machine learning when machine learning is beyond local IT capabilities.
Starting Page	79
Ending Page	96
Page Count	18
File Format	PDF
ISSN	07484518
Journal	Journal of Quantitative Criminology
Volume Number	30
Issue Number	1
e-ISSN	15737799
Language	English
Publisher	Springer US
Publisher Date	2013-02-17
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Sentencing Forecasting Machine learning Classification trees Criminology & Criminal Justice Sociology Methodology of the Social Sciences Statistics
Content Type	Text
Resource Type	Article
Subject	Law Pathology and Forensic Medicine

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