What Can Machines Learn, and What Does It Mean for Occupations and the Economy?
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pandp.20181019.pdf
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636.06 KB
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Adobe PDF
Checksum (MD5)
7a5d4b8b60727240efcffdf419278710
Author(s)
Brynjolfsson, Erik
Mitchell, Tom
Rock, Daniel
Date Issued
May 2018
Journal
AEA Papers and Proceedings
Publisher
American Economic Association
Citation
Brynjolfsson, Erik, Tom Mitchell, and Daniel Rock. “What Can Machines Learn, and What Does It Mean for Occupations and the Economy?” AEA Papers and Proceedings 108 (2018): 43–47.
Version
Final published version
Abstract
Advances in machine learning (ML) are poised to transform numerous occupations and industries. This raises the question of which tasks will be most affected by ML. We apply the rubric evaluating task potential for ML in Brynjolfsson and Mitchell (2017) to build measures of "Suitability for Machine Learning" (SML) and apply it to 18,156 tasks in O*NET. We find that (i) ML affects different occupations than earlier automation waves; (ii) most occupations include at least some SML tasks; (iii) few occupations are fully automatable using ML; and (iv) realizing the potential of ML usually requires redesign of job task content.
MIT Department
Sloan School of Management
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1257/PANDP.20181019
