Does Machine Learning Automate Moral Hazard and Error?
Sendhil Mullainathan and
American Economic Review, 2017, vol. 107, issue 5, 476-80
Machine learning tools are beginning to be deployed en masse in health care. While the statistical underpinnings of these techniques have been questioned with regard to causality and stability, we highlight a different concern here, relating to measurement issues. A characteristic feature of health data, unlike other applications of machine learning, is that neither y nor x is measured perfectly. Far from a minor nuance, this can undermine the power of machine learning algorithms to drive change in the health care system--and indeed, can cause them to reproduce and even magnify existing errors in human judgment.
JEL-codes: D82 I11 I18 (search for similar items in EconPapers)
Note: DOI: 10.1257/aer.p20171084
References: Add references at CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
https://www.aeaweb.org/articles/attachments?retrie ... wcFWHMZdiA4GAhqjztC6 (application/zip)
Access to full text is restricted to AEA members and institutional subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:aea:aecrev:v:107:y:2017:i:5:p:476-80
Ordering information: This journal article can be ordered from
Access Statistics for this article
American Economic Review is currently edited by Esther Duflo
More articles in American Economic Review from American Economic Association Contact information at EDIRC.
Bibliographic data for series maintained by Michael P. Albert ().