IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i17p10594-d897198.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/17/10594/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/17/10594/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Bansak, Kirk, 2019. "Can nonexperts really emulate statistical learning methods? A comment on “The accuracy, fairness, and limits of predicting recidivismâ€," Political Analysis, Cambridge University Press, vol. 27(3), pages 370-380, July.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kigerl, Alex & Hamilton, Zachary & Kowalski, Melissa & Mei, Xiaohan, 2022. "The great methods bake-off: Comparing performance of machine learning algorithms," Journal of Criminal Justice, Elsevier, vol. 82(C).
    2. Jiaming Zeng & Berk Ustun & Cynthia Rudin, 2017. "Interpretable classification models for recidivism prediction," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 689-722, June.
    3. Bhatt, Monica & Heller, Sara & Kapustin, Max & Bertrand, Marianne & Blattman, Christopher, 2023. "Predicting and Preventing Gun Violence: An Experimental Evaluation of READI Chicago," SocArXiv dks29, Center for Open Science.
    4. Valasik, Matthew, 2018. "Gang violence predictability: Using risk terrain modeling to study gang homicides and gang assaults in East Los Angeles," Journal of Criminal Justice, Elsevier, vol. 58(C), pages 10-21.
    5. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
    6. Vahlne, Niklas, 2017. "On LPG usage in rural Vietnamese households," Development Engineering, Elsevier, vol. 2(C), pages 1-11.
    7. Richard A. Berk & Susan B. Sorenson & Geoffrey Barnes, 2016. "Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 13(1), pages 94-115, March.
    8. Raffaele Mattera & Philipp Otto, 2023. "Network log-ARCH models for forecasting stock market volatility," Papers 2303.11064, arXiv.org.
    9. Paul Seed, 2010. "The use of cost information when defining critical values for prediction of rare events by using logistic regression and similar methods," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 255-256, January.
    10. Bansak, Kirk & Paulson, Elisabeth, 2023. "Public Opinion on Fairness and Efficiency for Algorithmic and Human Decision-Makers," OSF Preprints pghmx, Center for Open Science.
    11. Ciner, Cetin, 2019. "Do industry returns predict the stock market? A reprise using the random forest," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 152-158.
    12. Brendan O'Flaherty & Rajiv Sethi & Morgan Williams, 2024. "The nature, detection, and avoidance of harmful discrimination in criminal justice," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 43(1), pages 289-320, January.
    13. Kalist David E. & Lee Daniel Y. & Spurr Stephen J., 2015. "Predicting Recidivism of Juvenile Offenders," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 15(1), pages 1-23, January.
    14. Cynthia Rudin & Berk Ustun, 2018. "Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice," Interfaces, INFORMS, vol. 48(5), pages 449-466, October.
    15. Sharad Goel & Justin M. Rao & Ravi Shroff, 2016. "Personalized Risk Assessments in the Criminal Justice System," American Economic Review, American Economic Association, vol. 106(5), pages 119-123, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10594-:d:897198. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.