RT Article
T1 An impact assessment of machine learning risk forecasts on parole board decisions and recidivism
JF Journal of experimental criminology
VO 13
IS 2
SP 193
OP 216
A1 Berk, Richard
LA English
YR 2017
UL https://krimdok.uni-tuebingen.de/Record/1747983438
AB 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 and subsequent recidivism.
K1 Parole·Machine learning
K1 Recidivism
K1 Forecasting
K1 Regression discontinuity design
K1 Multinomial logistic regression
DO 10.1007/s11292-017-9286-2