Comparative Advantage of Humans versus AI in the Long Tail

Nikhil Agarwal, Ray Huang, Alex Moehring, Pranav Rajpurkar, Tobias Salz and Feiyang Yu

AEA Papers and Proceedings, 2024, vol. 114, 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.

JEL-codes: C45 D83 I11 I12 J63 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.aeaweb.org/doi/10.1257/pandp.20241071 (application/pdf)
https://doi.org/10.3886/E202185V1 (text/html)
https://www.aeaweb.org/doi/10.1257/pandp.20241071.appx (application/pdf)
https://www.aeaweb.org/doi/10.1257/pandp.20241071.ds (application/zip)
Access to full text is restricted to AEA members and institutional subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aea:apandp:v:114:y:2024:p:618-22

Ordering information: This journal article can be ordered from
https://www.aeaweb.org/subscribe.html

DOI: 10.1257/pandp.20241071

Access Statistics for this article

AEA Papers and Proceedings is currently edited by William Johnson and Kelly Markel

More articles in AEA Papers and Proceedings from American Economic Association Contact information at EDIRC.
Bibliographic data for series maintained by Michael P. Albert ().