AEA Papers and Proceedings
ISSN 2574-0768 (Print) | ISSN 2574-0776 (Online)
Comparative Advantage of Humans versus AI in the Long Tail
AEA Papers and Proceedings
vol. 114,
May 2024
(pp. 618–22)
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.Citation
Agarwal, Nikhil, Ray Huang, Alex Moehring, Pranav Rajpurkar, Tobias Salz, and Feiyang Yu. 2024. "Comparative Advantage of Humans versus AI in the Long Tail." AEA Papers and Proceedings, 114: 618–22. DOI: 10.1257/pandp.20241071Additional Materials
JEL Classification
- C45 Neural Networks and Related Topics
- D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
- I11 Analysis of Health Care Markets
- I12 Health Behavior
- J63 Labor Turnover; Vacancies; Layoffs