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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. 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.
    3. 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.
    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. Jon Kleinberg & Sendhil Mullainathan, 2019. "Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability," NBER Working Papers 25854, National Bureau of Economic Research, Inc.
    5. 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.
    6. 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.
    7. 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.
    8. Shams Mehdi & Pratyush Tiwary, 2024. "Thermodynamics-inspired explanations of artificial intelligence," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    9. 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.
    10. Kai Feng & Han Hong & Ke Tang & Jingyuan Wang, 2023. "Statistical Tests for Replacing Human Decision Makers with Algorithms," Papers 2306.11689, arXiv.org.
    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.

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