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Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice

Author

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  • Cynthia Rudin

    (Departments of Computer Science, Electrical and Computer Engineering, and Statistical Science, Duke University, Durham, North Carolina 27708)

  • Berk Ustun

    (Center for Research in Computation for Society, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138)

Abstract

Questions of trust in machine-learning models are becoming increasingly important as these tools are starting to be used widely for high-stakes decisions in medicine and criminal justice. Transparency of models is a key aspect affecting trust. This paper reveals that there is new technology to build transparent machine-learning models that are often as accurate as black-box machine-learning models. These methods have already had an impact in medicine and criminal justice. This work calls into question the overall need for black-box models in these applications.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orinte:v:48:y:2018:i:5:p:449-466
    DOI: 10.1287/inte.2018.0957
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    References listed on IDEAS

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    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. 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.
    4. 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.
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    Cited by:

    1. Ramos Maqueda,Manuel & Chen,Daniel Li, 2021. "The Role of Justice in Development : The Data Revolution," Policy Research Working Paper Series 9720, The World Bank.

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