What Can Machines Learn, and What Does It Mean for Occupations and the Economy?

Brynjolfsson, Erik, Tom Mitchell, and Daniel Rock. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?." In AEA Papers and Proceedings, vol. 108, pp. 43-47. 2018.

8 Pages Posted: 15 Aug 2018

See all articles by Erik Brynjolfsson

Erik Brynjolfsson

National Bureau of Economic Research (NBER); Stanford

Tom Mitchell

Carnegie Mellon University

Daniel Rock

University of Pennsylvania - Operations & Information Management Department

Date Written: May 1, 2018

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:
1) ML affects different occupations than earlier automation waves,
2) most occupations include at least some SML tasks,
3) few occupations are fully automatable using ML, and
4) realizing the potential of ML usually requires redesign of job task content.

Keywords: machine learning, occupational change, technology, job design

JEL Classification: J23, J24, O33

Suggested Citation

Brynjolfsson, Erik and Mitchell, Tom and Rock, Daniel, What Can Machines Learn, and What Does It Mean for Occupations and the Economy? (May 1, 2018). Brynjolfsson, Erik, Tom Mitchell, and Daniel Rock. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?." In AEA Papers and Proceedings, vol. 108, pp. 43-47. 2018., Available at SSRN: https://ssrn.com/abstract=3224100

Erik Brynjolfsson

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Stanford ( email )

366 Galvez St
Stanford, CA 94305
United States

HOME PAGE: http://brynjolfsson.com

Tom Mitchell

Carnegie Mellon University ( email )

Pittsburgh, PA 15213-3890
United States

Daniel Rock (Contact Author)

University of Pennsylvania - Operations & Information Management Department ( email )

Philadelphia, PA 19104
United States

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