Forecasts of Violence to Inform Sentencing Decisions



Richard Berk, Justin Bleich

Journal of Quantitative Criminology

March 2014, Volume 30, Issue 1, pp 79-96




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.