Predicting Recidivism to Drug Distribution using Machine Learning Techniques

Butsara, Nuttawit; Athonthitichot, Panchan; Jodpimai, Pichai

doi:10.1109/ictke47035.2019.8966834

Cited by 3 publications

(9 citation statements)

References 6 publications

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“…Thus, the authors took advantage of the statistical models using several predictor variables, including historical risk assessment data and a rich set of developmental factors for all youth reported for delinquency by the Florida Department of Juvenile Justice (FDJJ). The second study published by Butsara [ 21 ] used a dataset from a central correctional institution for drug addicts and a central women’s correctional institution in Thailand. The sample consists in 300 male and 298 female inmates.…”

Section: Resultsmentioning

confidence: 99%

“…Two of them preferred a feature selection technique [ 24 , 26 ]. One study used a generic data standardization and then applied feature selection [ 21 ]. The last one used ANOVA to identify relevant attributes for the current dataset [ 28 ].…”

Section: Resultsmentioning

confidence: 99%

“…The sorting leads to four categories: general [ 22 , 23 , 24 , 28 , 31 , 32 ], sexual [ 31 , 32 ], violent [ 22 , 25 , 31 ] and all other recidivism. The last category includes studies that considered a specific type of crime [ 21 ] or referred only to males [ 27 ] or youth [ 26 , 29 , 30 ].…”

Section: Resultsmentioning

confidence: 99%

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

Travaini

Pacchioni

Bellumore

et al. 2022

IJERPH

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

Travaini

Pacchioni

Bellumore

et al. 2022

IJERPH

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“…The armed robbery case we took inspiration from is the case of B.C. 4 To avoid disclosing the case, we named B.C. Valjean and we used other names from Les Misérables by Victor Hugo for other actors.…”

Section: Introductionmentioning

confidence: 99%

“…Using machine learning for boosting predictive justice is becoming a very lively research field, although in many cases the developed applications are academic prototypes, not yet used in real trials. Applications range from predicting decisions of the European Court of Human Rights [11] to predicting recidivism of many different crimes [4], to risk assessment in criminal justice [2].…”

Section: Introductionmentioning

confidence: 99%

Logical Judges Challenge Human Judges on the Strange Case of B.C.-Valjean

Mascardi

Pellegrini

2020

Electron. Proc. Theor. Comput. Sci.

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On May 12th, 2020, during the course entitled Artificial Intelligence and Jurisdiction Practice organized by the Italian School of Magistracy, more than 70 magistrates followed our demonstration of a Prolog logical judge reasoning on an armed robbery case. Although the implemented logical judge is just an exercise of knowledge representation and simple deductive reasoning, a practical demonstration of an automated reasoning tool to such a large audience of potential end-users represents a first and unique attempt in Italy and, to the best of our knowledge, in the international panorama. In this paper we present the case addressed by the logical judge-a real case already addressed by a human judge in 2015-and the feedback on the demonstration collected from the attendees.

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