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Interpretable classification models for recidivism prediction

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  • Jiaming Zeng
  • Berk Ustun
  • Cynthia Rudin

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  • Jiaming Zeng & Berk Ustun & Cynthia Rudin, 2017. "Interpretable classification models for recidivism prediction," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 689-722, June.
    • Handle: RePEc:bla:jorssa:v:180:y:2017:i:3:p:689-722
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    File URL: http://hdl.handle.net/10.1111/rssa.12227
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    References listed on IDEAS

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    1. Richard Berk & Lawrence Sherman & Geoffrey Barnes & Ellen Kurtz & Lindsay Ahlman, 2009. "Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 191-211, January.
    2. Hoffman, Peter B., 1994. "Twenty years of operational use of a risk prediction instrument: The United States parole commission's salient factor score," Journal of Criminal Justice, Elsevier, vol. 22(6), pages 477-494.
    3. N. Tollenaar & P. G. M. van der Heijden, 2013. "Which method predicts recidivism best?: a comparison of statistical, machine learning and data mining predictive models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(2), pages 565-584, February.
    4. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    Citations

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    Cited by:

    1. Xiaochen Hu & Xudong Zhang & Nicholas Lovrich, 2021. "Public perceptions of police behavior during traffic stops: logistic regression and machine learning approaches compared," Journal of Computational Social Science, Springer, vol. 4(1), pages 355-380, May.
    2. Toru Kitagawa & Shosei Sakaguchi & Aleksey Tetenov, 2021. "Constrained Classification and Policy Learning," Papers 2106.12886, arXiv.org, revised Jul 2023.
    3. Dragos Florin Ciocan & Velibor V. Mišić, 2022. "Interpretable Optimal Stopping," Management Science, INFORMS, vol. 68(3), pages 1616-1638, March.
    4. Carrizosa, Emilio & Ramírez-Ayerbe, Jasone & Romero Morales, Dolores, 2024. "Mathematical optimization modelling for group counterfactual explanations," European Journal of Operational Research, Elsevier, vol. 319(2), pages 399-412.
    5. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
    6. Shams Mehdi & Pratyush Tiwary, 2024. "Thermodynamics-inspired explanations of artificial intelligence," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    7. DeVall, Kristen E. & Gregory, Paul D. & Hartmann, David J., 2025. "The Problems (and possible solutions) of assessing risk, race and recidivism in long operating drug treatment courts," Evaluation and Program Planning, Elsevier, vol. 108(C).
    8. Margrét Vilborg Bjarnadóttir & David B. Anderson & Ritu Agarwal & D. Alan Nelson, 2022. "Aiding the prescriber: developing a machine learning approach to personalized risk modeling for chronic opioid therapy amongst US Army soldiers," Health Care Management Science, Springer, vol. 25(4), pages 649-665, December.
    9. Jon Kleinberg & Sendhil Mullainathan, 2019. "Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability," NBER Working Papers 25854, National Bureau of Economic Research, Inc.
    10. Kai Feng & Han Hong & Ke Tang & Jingyuan Wang, 2023. "Statistical Tests for Replacing Human Decision Makers with Algorithms," Papers 2306.11689, arXiv.org, revised Dec 2024.
    11. Cynthia Rudin & Berk Ustun, 2018. "Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice," Interfaces, INFORMS, vol. 48(5), pages 449-466, October.
    12. Kristian Lum & David B. Dunson & James Johndrow, 2022. "Closer than they appear: A Bayesian perspective on individual‐level heterogeneity in risk assessment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 588-614, April.
    13. Beau Coker & Cynthia Rudin & Gary King, 2021. "A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results," Management Science, INFORMS, vol. 67(10), pages 6174-6197, October.

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