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Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction

Author

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  • Guido Vittorio Travaini

    (School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy)

  • Federico Pacchioni

    (School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy)

  • Silvia Bellumore

    (School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy)

  • Marta Bosia

    (School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
    Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy)

  • Francesco De Micco

    (Bioethics and Humanities Research Unit, Campus Bio-Medico University of Rome, 00128 Rome, Italy
    Department of Clinical Affairs, Campus Bio-Medico University Hospital Foundation, 00128 Rome, Italy)

Abstract

Recent evolution in the field of data science has revealed the potential utility of machine learning (ML) applied to criminal justice. Hence, the literature focused on finding better techniques to predict criminal recidivism risk is rapidly flourishing. However, it is difficult to make a state of the art for the application of ML in recidivism prediction. In this systematic review, out of 79 studies from Scopus and PubMed online databases we selected, 12 studies that guarantee the replicability of the models across different datasets and their applicability to recidivism prediction. The different datasets and ML techniques used in each of the 12 studies have been compared using the two selected metrics. This study shows how each method applied achieves good performance, with an average score of 0.81 for ACC and 0.74 for AUC. This systematic review highlights key points that could allow criminal justice professionals to routinely exploit predictions of recidivism risk based on ML techniques. These include the presence of performance metrics, the use of transparent algorithms or explainable artificial intelligence (XAI) techniques, as well as the high quality of input data.

Suggested Citation

  • Guido Vittorio Travaini & Federico Pacchioni & Silvia Bellumore & Marta Bosia & Francesco De Micco, 2022. "Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction," IJERPH, MDPI, vol. 19(17), pages 1-13, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10594-:d:897198
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
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