Toward understanding the impact of artificial intelligence on labor

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Abstract

Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human–machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.

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Author and article information

Journal

Journal ID (nlm-ta): Proc Natl Acad Sci U S A

Journal ID (iso-abbrev): Proc. Natl. Acad. Sci. U.S.A

Journal ID (hwp): pnas

Journal ID (pmc): pnas

Journal ID (publisher-id): PNAS

Title: Proceedings of the National Academy of Sciences of the United States of America

Publisher: National Academy of Sciences

ISSN (Print): 0027-8424

ISSN (Electronic): 1091-6490

Publication date (Print): 2 April 2019

Publication date (Electronic): 25 March 2019

Publication date PMC-release: 25 March 2019

Volume: 116

Issue: 14

Pages: 6531-6539

Affiliations

[1] ^aMedia Laboratory, Massachusetts Institute of Technology , Cambridge, MA 02139;

[2] ^bDepartment of Economics, Massachusetts Institute of Technology , Cambridge, MA 02139;

[3] ^cTechnology & Policy Research Initiative, School of Law, Boston University , Boston, MA 02215;

[4] ^dSloan School of Management, Massachusetts Institute of Technology , Cambridge, MA 02139;

[5] ^eNational Bureau of Economic Research , Cambridge, MA 02138;

[6] ^fHarvard Kennedy School, Harvard University , Cambridge, MA 02138;

[7] ^gGraduate School of Education, Harvard University , Cambridge, MA 02138;

[8] ^hDepartment of Public Policy, The University of North Carolina at Chapel Hill , Chapel Hill, NC 27599;

[9] ⁱSchool of Sustainability, Arizona State University , Tempe, AZ 85287;

[10] ^jGrupo Interdisciplinar de Sistemas Complejos, Departmento de Matematicas, Escuela Politécnica Superior, Universidad Carlos III de Madrid , 28911 Madrid, Spain;

[11] ^kKellogg School of Management, Northwestern University , Evanston, IL 60208;

[12] ^lNorthwestern Institute on Complex Systems, Northwestern University , Evanston, IL 60208;

[13] ^mInstitute for Data, Systems, and Society, Massachusetts Institute of Technology , Cambridge, MA 02139;

[14] ⁿCenter for Humans and Machines, Max Planck Institute for Human Development , 14195 Berlin, Germany

Author notes

¹To whom correspondence should be addressed. Email: irahwan@ 123456mit.edu .

Edited by Jose A. Scheinkman, Columbia University, New York, NY, and approved February 28, 2019 (received for review January 18, 2019)

Author contributions: M.R.F., D.A., J.E.B., E.B., M.C., D.J.D., M.F., M.G., J.L., E.M., D.W., H.Y., and I.R. designed research; M.R.F. performed research; M.R.F. and M.G. analyzed data; and M.R.F., D.A., J.E.B., E.B., M.C., D.J.D., M.F., M.G., J.L., E.M., D.W., H.Y., and I.R. wrote the paper.