Data and Code for: Comparative Advantage of Humans vs AI in the Long Tail

Principal Investigator(s): View help for Principal Investigator(s) Nikhil Agarwal, Massachusetts Institute of Technology; Ray Huang, Massachusetts Institute of Technology; Alex Moehring, MIT Sloan School of Management; Pranav Rajpurkar, Harvard University. Harvard Medical School; Tobias Salz, Massachusetts Institute of Technology; Feiyang Yu, Harvard University

Version: View help for Version V1

Name	File Type	Size	Last Modified
Rad_AI_Longtail			05/10/2024 12:29:PM

Project Citation:

Agarwal, Nikhil, Huang, Ray, Moehring, Alex, Rajpurkar, Pranav, Salz, Tobias, and Yu, Feiyang. Data and Code for: Comparative Advantage of Humans vs AI in the Long Tail. Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-05-21. https://doi.org/10.3886/E202185V1

Project Description

Summary: Abstract: Machine learning algorithms now exceed human performance on a number of 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 are 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.

Scope of Project

Subject Terms: Randomized Control Trial; machine learning; radiology; long tail; labor; health

JEL Classification:
      I10 Health: General
      I11 Analysis of Health Care Markets
      J24 Human Capital; Skills; Occupational Choice; Labor Productivity

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V1 [2024-05-21]

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