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Toward understanding the impact of artificial intelligence on labor

Author

Listed:
  • Morgan R. Frank

    (Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139)

  • David Autor

    (Department of Economics, Massachusetts Institute of Technology, Cambridge, MA 02139)

  • James E. Bessen

    (Technology & Policy Research Initiative, School of Law, Boston University, Boston, MA 02215)

  • Erik Brynjolfsson

    (Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02139; National Bureau of Economic Research, Cambridge, MA 02138)

  • Manuel Cebrian

    (Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139)

  • David J. Deming

    (Harvard Kennedy School, Harvard University, Cambridge, MA 02138; Graduate School of Education, Harvard University, Cambridge, MA 02138)

  • Maryann Feldman

    (Department of Public Policy, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599)

  • Matthew Groh

    (Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139)

  • José Lobo

    (School of Sustainability, Arizona State University, Tempe, AZ 85287)

  • Esteban Moro

    (Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139; Grupo Interdisciplinar de Sistemas Complejos, Departmento de Matematicas, Escuela Politécnica Superior, Universidad Carlos III de Madrid, 28911 Madrid, Spain)

  • Dashun Wang

    (Kellogg School of Management, Northwestern University, Evanston, IL 60208; Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208)

  • Hyejin Youn

    (Kellogg School of Management, Northwestern University, Evanston, IL 60208; Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208)

  • Iyad Rahwan

    (Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139; Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139; Center for Humans and Machines, Max Planck Institute for Human Development, 14195 Berlin, Germany)

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.

Suggested Citation

  • Morgan R. Frank & David Autor & James E. Bessen & Erik Brynjolfsson & Manuel Cebrian & David J. Deming & Maryann Feldman & Matthew Groh & José Lobo & Esteban Moro & Dashun Wang & Hyejin Youn & Iyad Ra, 2019. "Toward understanding the impact of artificial intelligence on labor," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(14), pages 6531-6539, April.
    • Handle: RePEc:nas:journl:v:116:y:2019:p:6531-6539
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