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Comparative Advantage of Humans versus AI in the Long Tail

Author

Listed:
  • Nikhil Agarwal
  • Ray Huang
  • Alex Moehring
  • Pranav Rajpurkar
  • Tobias Salz
  • Feiyang Yu

Abstract

Machine learning algorithms now exceed human performance on several predictive tasks, generating concerns about widespread job displacement. However, supervised learning approaches rely on large amounts of high-quality labeled data and are designed for specific predictive tasks. Thus, humans may be required for a large number of tasks, each of which is not commonly encountered—the long tail—because humans can make predictions for a broader range of outcomes and with exposure to much less data. We show that a self-supervised algorithm for chest X-rays, which does not require specifically annotated disease labels, closes this gap even in the long tail of diseases.

Suggested Citation

  • Nikhil Agarwal & Ray Huang & Alex Moehring & Pranav Rajpurkar & Tobias Salz & Feiyang Yu, 2024. "Comparative Advantage of Humans versus AI in the Long Tail," AEA Papers and Proceedings, American Economic Association, vol. 114, pages 618-622, May.
  • Handle: RePEc:aea:apandp:v:114:y:2024:p:618-22
    DOI: 10.1257/pandp.20241071
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    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs

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