Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction
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- Richard Berk & Lawrence Sherman & Geoffrey Barnes & Ellen Kurtz & Lindsay Ahlman, 2009. "Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 191-211, January.
- Nikolaj Tollenaar & Peter G M van der Heijden, 2019. "Optimizing predictive performance of criminal recidivism models using registration data with binary and survival outcomes," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-37, March.
- Bansak, Kirk, 2019. "Can nonexperts really emulate statistical learning methods? A comment on “The accuracy, fairness, and limits of predicting recidivism”," Political Analysis, Cambridge University Press, vol. 27(3), pages 370-380, July.
- N. Tollenaar & P. G. M. van der Heijden, 2013. "Which method predicts recidivism best?: a comparison of statistical, machine learning and data mining predictive models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(2), pages 565-584, February.
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Keywords
machine learning; recidivism; crime prediction; artificial intelligence; explainable artificial intelligence;All these keywords.
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