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15-03-2023 | Original Research

Predicting inmates misconduct using the SHAP approach

Authors: Fábio M. Oliveira, Marcelo S. Balbino, Luis E. Zarate, Fawn Ngo, Ramakrishna Govindu, Anurag Agarwal, Cristiane N. Nobre

Published in: Artificial Intelligence and Law

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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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While some authors differentiate interpretability and explainability (Gilpin et al. 2018; Rudin 2019), others use them interchangeably (Silva et al. 2018; Carvalho et al. 2019; Molnar 2020). In this work, we use the words interchangeably in the sense of understandability in human terms.

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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Title: Predicting inmates misconduct using the SHAP approach
Authors: Fábio M. Oliveira
Marcelo S. Balbino
Luis E. Zarate
Fawn Ngo
Ramakrishna Govindu
Anurag Agarwal
Cristiane N. Nobre
Publication date: 15-03-2023
Publisher: Springer Netherlands
Published in: Artificial Intelligence and Law
Print ISSN: 0924-8463
Electronic ISSN: 1572-8382
DOI: https://doi.org/10.1007/s10506-023-09352-z

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