Predicting inmates misconduct using the SHAP approach

Author:

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

Publisher

Springer Science and Business Media LLC

Subject

Law,Artificial Intelligence

Reference111 articles.

1. Aas K, Jullum M, Løland A (2021) Explaining individual predictions when features are dependent: more accurate approximations to shapley values. Artif Intell 298(103):502

2. Adadi A, Berrada M (2018) Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access 6:52,138-52,160

3. Alper M, Durose MR, Markman J (2018) 2018 update on prisoner recidivism: A 9-year follow-up period (2005-2014). Tech. rep., U.S. Department of Justice - Office of Justice Programs - Bureau of Justice Statistics, https://www.bjs.gov/index.cfm?ty=pbdetail &iid=6266

4. Augustyn RA, ten Bensel T, Lytle RD et al (2020) “Older’’ inmates in prison: considering the tipping point of age and misconduct. Criminol Crim Just L & Soc’y 21:1

5. Bell K (2018) Prison violence and the intersectionality of race/ethnicity and gender. Actual Prob Econ Law. https://doi.org/10.21202/1993-047X.12.2018.1.132-148

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