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On the Inequity of Predicting A While Hoping for B

Author

Listed:
  • Sendhil Mullainathan
  • Ziad Obermeyer

Abstract

Algorithms trained to predict mismeasured proxy variables can reproduce and scale up racial bias. This mechanism of algorithmic bias is distinct from others in the literature and harder to detect. We show this using examples from health care, but the forces we consider apply to a range of other important social sectors.

Suggested Citation

  • Sendhil Mullainathan & Ziad Obermeyer, 2021. "On the Inequity of Predicting A While Hoping for B," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 37-42, May.
    • Handle: RePEc:aea:apandp:v:111:y:2021:p:37-42
    DOI: 10.1257/pandp.20211078
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    More about this item

    JEL classification:

    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality

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