WP 2016-4.0
Richard A. Berk
Objectives: The Pennsylvania Board of Probation and Parole has begun using machine learning forecasts to help inform parole release decisions. In this paper, we evaluate the impact of the forecasts on those decisions. In this paper, we evaluate the impact of the forecasts on those decisions and subsequent recidivism.
Methods: A close approximation to a natural, randomized experiment is used to evaluate the impact of the forecasts on parole release decisions. A generalized regression discontinuity design is used to evaluate the impact of the forecasts on recidivism.
Results: The forecasts apparently had no effect on the overall parole release rate, but did appear to alter the mix of inmates released. Important distinctions were made between offenders forecasted to be rearrested for nonviolent crime and offenders forecasted to be rearrested for violent crime. The balance of evidence indicates that the forecasts led to reductions in rearrests for both nonviolent and violent crimes.
Conclusions: Risk assessments based on machine learning forecasts can improve parole release decisions, especially when distinctions are made between rearrests for violent and nonviolent crime.
