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      Toward understanding the impact of artificial intelligence on labor


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          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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          Most cited references53

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          The Skill Content of Recent Technological Change: An Empirical Exploration

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            The product space conditions the development of nations.

            Economies grow by upgrading the products they produce and export. The technology, capital, institutions, and skills needed to make newer products are more easily adapted from some products than from others. Here, we study this network of relatedness between products, or "product space," finding that more-sophisticated products are located in a densely connected core whereas less-sophisticated products occupy a less-connected periphery. Empirically, countries move through the product space by developing goods close to those they currently produce. Most countries can reach the core only by traversing empirically infrequent distances, which may help explain why poor countries have trouble developing more competitive exports and fail to converge to the income levels of rich countries.
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              Why Are There Still So Many Jobs? The History and Future of Workplace Automation


                Author and article information

                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                2 April 2019
                25 March 2019
                25 March 2019
                : 116
                : 14
                : 6531-6539
                [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.

                Author information
                Copyright © 2019 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                Page count
                Pages: 9
                Social Sciences
                Economic Sciences

                automation,employment,economic resilience,future of work


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