Abstract
Internal misconduct is a universal problem in prisons and affects the maintenance of social order. Consequently, correctional institutions often develop rehabilitation programs to reduce the likelihood of inmates committing internal offenses and criminal recidivism after release. Therefore, it is necessary to identify the profile of each offender, both for the appropriate indication of a rehabilitation program and the level of internal security to which he must be submitted. In this context, this work aims to discover the most significant characteristics in predicting inmate misconduct from ML methods and the SHAP approach. A database produced in 2004 through the Survey of Inmates in State and Federal Correctional Facilities in the United States of America was used, which provides nationally representative data on prisoners from state and federal facilities. The predictive model based on Random Forest performed the best, thus, we applied the SHAP to it. Overall, the results showed that features related to victimization, type of crime committed, age and age at first arrest, history of association with criminal groups, education, and drug and alcohol use are most relevant in predicting internal misconduct. Thus, it is expected to contribute to the prior classification of an inmate on time, to use programs and practices that aim to improve the lives of offenders, their reintegration into society, and consequently, the reduction of criminal recidivism.
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Notes
Agnostic method of explanation is defined as one that is independent of the original model type Carvalho et al. (2019).
In 2016, the survey was renamed the Survey of Prison Inmates (SPI) (of Justice Statistics 2021).
We applied the library Missingpy in Python.
Available at https://jupyter.org/.
For the Decision Tree, we use the following hyperparameters: criterion=’entropy’, maximum depth = 100. All other parameters are default.
For the Random Forest, we use the following hyperparameters: criterion=’entropy’, n_estimators = 100. All other parameters are default.
For the Multilayer Perceptron, we use the following hyperparameters: hidden_layer_sizes=15, learning_rate=0.2, momentum=0.3. All other parameters are default.
For the Support Vector Machine, we use the following hyperparameters: probability=True, degree=1, gamma=scale, kernel =rbf. All other parameters are default.
Available at https://scikit-learn.org/.
\(\textit{Precision} = \frac{TP}{TP + FP}\)
\(\textit{Recall} = \frac{TP}{TP + FN}\)
\(\textit{F1-Score}\) = \(2 \times \frac{Precision\; \times\; Recall}{Precision\; + \;Recall}\)
Dependence Plot, available in SHAP.
Force Plot, available in SHAP.
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Acknowledgements
The authors thank the National Council for Scientific and Technological Development of Brazil (CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico), the Coordination for the Improvement of Higher Education Personnel - Brazil (CAPES) - Finance Code 001, the Foundation for Research Support of Minas Gerais State (FAPEMIG), and Federal Center for Technological Education of Minas Gerais (CEFET-MG). The work was developed at the Pontifical Catholic University of Minas Gerais in the Applied Computational Intelligence laboratory – LICAP.
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Oliveira, F.M., Balbino, M.S., Zarate, L.E. et al. Predicting inmates misconduct using the SHAP approach. Artif Intell Law (2023). https://doi.org/10.1007/s10506-023-09352-z
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DOI: https://doi.org/10.1007/s10506-023-09352-z