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

Guido Vittorio Travaini, Federico Pacchioni, Silvia Bellumore, Marta Bosia and Francesco De Micco ()
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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
Francesco De Micco: Bioethics and Humanities Research Unit, Campus Bio-Medico University of Rome, 00128 Rome, Italy

IJERPH, 2022, vol. 19, issue 17, 1-13

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.

Keywords: machine learning; recidivism; crime prediction; artificial intelligence; explainable artificial intelligence (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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