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Does Machine Learning Automate Moral Hazard and Error?

Author

Listed:
  • Sendhil Mullainathan
  • Ziad Obermeyer

Abstract

Machine learning tools are beginning to be deployed en masse in health care. While the statistical underpinnings of these techniques have been questioned with regard to causality and stability, we highlight a different concern here, relating to measurement issues. A characteristic feature of health data, unlike other applications of machine learning, is that neither y nor x is measured perfectly. Far from a minor nuance, this can undermine the power of machine learning algorithms to drive change in the health care system--and indeed, can cause them to reproduce and even magnify existing errors in human judgment.

Suggested Citation

  • Sendhil Mullainathan & Ziad Obermeyer, 2017. "Does Machine Learning Automate Moral Hazard and Error?," American Economic Review, American Economic Association, vol. 107(5), pages 476-480, May.
    • Handle: RePEc:aea:aecrev:v:107:y:2017:i:5:p:476-80
    Note: DOI: 10.1257/aer.p20171084
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    Cited by:

    1. Wayne Xinwei Wan & Thies Lindenthal, 2023. "Testing machine learning systems in real estate," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(3), pages 754-778, May.
    2. Persson, Petra & Qiu, Xinyao & Rossin-Slater, Maya, 2021. "Family Spillover Effects of Marginal Diagnoses: The Case of ADHD," IZA Discussion Papers 14020, Institute of Labor Economics (IZA).
    3. Persson, Petra & Qiu, Xinyao & Rossin-Slater, Maya, 2021. "Family Spillover Effects of Marginal Diagnoses: The Case of ADHD," CEPR Discussion Papers 15660, C.E.P.R. Discussion Papers.
    4. Ziyuan Wang, 2023. "Spatial Differentiation Characteristics of Rural Areas Based on Machine Learning and GIS Statistical Analysis—A Case Study of Yongtai County, Fuzhou City," Sustainability, MDPI, vol. 15(5), pages 1-18, March.
    5. Scott Duke Kominers & Alexander Teytelboym & Vincent P Crawford, 2017. "An invitation to market design," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 33(4), pages 541-571.
    6. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
    7. Songul Tolan, 2018. "Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges," JRC Working Papers on Digital Economy 2018-10, Joint Research Centre.
    8. Markus Eyting, 2020. "A Random Forest a Day Keeps the Doctor Away," Working Papers 2026, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    9. Yves Paul Vincent Mbous & Todd Brothers & Mohammad A. Al-Mamun, 2023. "Medication Regimen Complexity Index Score at Admission as a Predictor of Inpatient Outcomes: A Machine Learning Approach," IJERPH, MDPI, vol. 20(4), pages 1-16, February.
    10. Bo Cowgill, 2019. "Bias and Productivity in Humans and Machines," Upjohn Working Papers 19-309, W.E. Upjohn Institute for Employment Research.
    11. Bauer, Kevin & Pfeuffer, Nicolas & Abdel-Karim, Benjamin M. & Hinz, Oliver & Kosfeld, Michael, 2020. "The terminator of social welfare? The economic consequences of algorithmic discrimination," SAFE Working Paper Series 287, Leibniz Institute for Financial Research SAFE.
    12. Navitha Singh Sewpersadh, 2023. "Disruptive business value models in the digital era," Journal of Innovation and Entrepreneurship, Springer, vol. 12(1), pages 1-27, December.
    13. Laura Blattner & Scott Nelson & Jann Spiess, 2021. "Unpacking the Black Box: Regulating Algorithmic Decisions," Papers 2110.03443, arXiv.org, revised Jul 2023.
    14. Sendhil Mullainathan & Ziad Obermeyer, 2019. "Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care," NBER Working Papers 26168, National Bureau of Economic Research, Inc.
    15. van Giffen, Benjamin & Herhausen, Dennis & Fahse, Tobias, 2022. "Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods," Journal of Business Research, Elsevier, vol. 144(C), pages 93-106.
    16. Hamsa Bastani, 2021. "Predicting with Proxies: Transfer Learning in High Dimension," Management Science, INFORMS, vol. 67(5), pages 2964-2984, May.
    17. Jill Walker Rettberg, 2020. "Situated data analysis: a new method for analysing encoded power relationships in social media platforms and apps," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-13, December.
    18. Wan, Wayne Xinwei & Lindenthal, Thies, 2022. "Towards accountability in machine learning applications: A system-testing approach," ZEW Discussion Papers 22-001, ZEW - Leibniz Centre for European Economic Research.

    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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