Toward understanding the impact of artificial intelligence on labor

Morgan R. Frank, David Autor, James Bessen, Erik Brynjolfsson, Manuel Cebrian, David Deming, Maryann Feldman, Matthew Groh, José Lobo, Esteban Moro, Dashun Wang, Hyejin Youn and Iyad Rahwan ()
Additional contact information
Morgan R. Frank: Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139
Manuel Cebrian: Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139
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

Proceedings of the National Academy of Sciences, 2019, vol. 116, issue 14, 6531-6539

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.

Keywords: automation; employment; economic resilience; future of work (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:116:y:2019:p:6531-6539

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