Projecting Violent Re-Offending in a Parole Population: Developing a Real-Time Forecasting Procedure to Inform Parole Decision-Making, Pennsylvania, 2012-2014 (ICPSR 36432)
Version Date: Feb 10, 2022 View help for published
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Richard A. Berk, University of Pennsylvania
https://doi.org/10.3886/ICPSR36432.v1
Version V1
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The University of Pennsylvania, in collaboration with the Pennsylvania Board of Probation and Parole (PBPP), began developing a violent forecast model utilizing the machine learning procedure random forest. By the spring of 2013, the forecasts were provided to decision makers prior to parole interviews. The violent forecast model (VFM) measures the extent to which offenders are likely to reoffend as indicated by future arrest. The VFM is a violence classification forecast and not an individual case prediction regarding offender behavior. Purpose The purpose of this research was to evaluate the impact of introducing forecasts of "future dangerousness" into PBPP's decision making process during parole interviews. The researcher anticipated that having available a sufficiently reliable forecast, particularly within the violent category, would reduce the likelihood of a parole release. The null hypothesis tested was that there would be no difference in parole release decisions when comparing two similar groups of offenders where during one group of parole interviews the decision maker had a forecast available and the other group of interviews there was not a forecast available.
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Access to these data is restricted. Users interested in obtaining these data must complete a Restricted Data Use Agreement, specify the reason for the request, and obtain IRB approval or notice of exemption for their research.
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The violence forecasts are developed using the machine learning procedure random forests.
Study Purpose View help for Study Purpose
The purpose of this research was to evaluate the impact of introducing forecasts of "future dangerousness" into the Pennsylvania Board of Probation and Parole's decision making process during parole interviews.
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The population consisted of offenders who were interviewed for parole. A quasi-experimental design approach was utilized as some decision makers were provided the forecasts prior to the interviews, while others were not. Random sampling in which the treatment group were the interviewed parolees whose forecasts were available at the time of the interview were compared to the control group who did not have a forecast at the time of interview.
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Pennsylvania Parolees
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