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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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    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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