175 lines
5.2 KiB
YAML
175 lines
5.2 KiB
YAML
abstract: 'Rapid advances in artificial intelligence (AI) and automation
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technologies have the potential to significantly disrupt labor markets.
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While AI and automation can augment the productivity of some workers,
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they can replace the work done by others and will likely transform
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almost all occupations at least to some degree. Rising automation is
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happening in a period of growing economic inequality, raising fears of
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mass technological unemployment and a renewed call for policy efforts to
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address the consequences of technological change. In this paper we
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discuss the barriers that inhibit scientists from measuring the effects
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of AI and automation on the future of work. These barriers include the
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lack of high-quality data about the nature of work (e.g., the dynamic
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requirements of occupations), lack of empirically informed models of key
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microlevel processes (e.g., skill substitution and human-machine
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complementarity), and insufficient understanding of how cognitive
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technologies interact with broader economic dynamics and institutional
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mechanisms (e.g., urban migration and international trade policy).
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Overcoming these barriers requires improvements in the longitudinal and
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spatial resolution of data, as well as refinements to data on workplace
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skills. These improvements will enable multidisciplinary research to
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quantitatively monitor and predict the complex evolution of work in
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tandem with technological progress. Finally, given the fundamental
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uncertainty in predicting technological change, we recommend developing
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a decision framework that focuses on resilience to unexpected scenarios
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in addition to general equilibrium behavior.'
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affiliation: 'Rahwan, I (Corresponding Author), MIT, Media Lab, Cambridge, MA 02139
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USA.
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Rahwan, I (Corresponding Author), MIT, Inst Data Syst \& Soc, 77 Massachusetts Ave,
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Cambridge, MA 02139 USA.
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Rahwan, I (Corresponding Author), Max Planck Inst Human Dev, Ctr Humans \& Machines,
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D-14195 Berlin, Germany.
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Frank, Morgan R.; Cebrian, Manuel; Groh, Matthew; Moro, Esteban; Rahwan, Iyad, MIT,
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Media Lab, Cambridge, MA 02139 USA.
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Autor, David, MIT, Dept Econ, Cambridge, MA 02139 USA.
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Bessen, James E., Boston Univ, Sch Law, Technol \& Policy Res Initiat, Boston, MA
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02215 USA.
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Brynjolfsson, Erik, MIT, Sloan Sch Management, 77 Massachusetts Ave, Cambridge,
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MA 02139 USA.
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Brynjolfsson, Erik, Natl Bur Econ Res, Cambridge, MA 02138 USA.
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Deming, David J., Harvard Univ, Harvard Kennedy Sch, Cambridge, MA 02138 USA.
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Deming, David J., Harvard Univ, Grad Sch Educ, Cambridge, MA 02138 USA.
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Feldman, Maryann, Univ N Carolina, Dept Publ Policy, Chapel Hill, NC 27599 USA.
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Lobo, Jose, Arizona State Univ, Sch Sustainabil, Tempe, AZ 85287 USA.
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Moro, Esteban, Univ Carlos III Madrid, Escuela Politecn Super, Dept Matemat, Grp
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Interdisciplinar Sistemas Complejos, Madrid 28911, Spain.
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Wang, Dashun; Youn, Hyejin, Northwestern Univ, Kellogg Sch Management, Evanston,
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IL 60208 USA.
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Wang, Dashun; Youn, Hyejin, Northwestern Univ, Northwestern Inst Complex Syst, Evanston,
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IL 60208 USA.
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Rahwan, Iyad, MIT, Inst Data Syst \& Soc, 77 Massachusetts Ave, Cambridge, MA 02139
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USA.
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Rahwan, Iyad, Max Planck Inst Human Dev, Ctr Humans \& Machines, D-14195 Berlin,
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Germany.'
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author: Frank, Morgan R. and Autor, David and Bessen, James E. and Brynjolfsson, Erik
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and Cebrian, Manuel and Deming, David J. and Feldman, Maryann and Groh, Matthew
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and Lobo, Jose and Moro, Esteban and Wang, Dashun and Youn, Hyejin and Rahwan, Iyad
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author-email: irahwan@mit.edu
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author_list:
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- family: Frank
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given: Morgan R.
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- family: Autor
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given: David
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- family: Bessen
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given: James E.
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- family: Brynjolfsson
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given: Erik
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- family: Cebrian
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given: Manuel
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- family: Deming
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given: David J.
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- family: Feldman
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given: Maryann
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- family: Groh
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given: Matthew
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- family: Lobo
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given: Jose
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- family: Moro
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given: Esteban
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- family: Wang
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given: Dashun
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- family: Youn
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given: Hyejin
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- family: Rahwan
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given: Iyad
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da: '2023-09-28'
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doi: 10.1073/pnas.1900949116
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eissn: 1091-6490
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esi-highly-cited-paper: Y
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esi-hot-paper: N
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files: []
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issn: 0027-8424
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journal: 'PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF
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AMERICA'
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keywords: automation; employment; economic resilience; future of work
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keywords-plus: SKILL; FUTURE; TASKS; JOBS; PROFESSION; EMPLOYMENT; DEMANDS; GROWTH
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language: English
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month: APR 2
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number: '14'
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number-of-cited-references: '85'
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orcid-numbers: 'Rahwan, Iyad/0000-0002-1796-4303
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Moro, Esteban/0000-0003-2894-1024
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Youn, Hyejin/0000-0002-6190-4412
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Lobo, Jose/0000-0002-0814-7168
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/0000-0001-9487-9359
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/0000-0002-6915-9381
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Groh, Matthew/0000-0002-9029-0157'
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pages: 6531-6539
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papis_id: 6be6fb5f2bb6a333ec3e47263a7895e5
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ref: Frank2019understandingimpact
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researcherid-numbers: 'Rahwan, Iyad/ABB-2422-2020
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Frank, Morgan R/L-3124-2016
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Moro, Esteban/AAB-1159-2019
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Youn, Hyejin/ABD-2997-2020
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Lobo, Jose/AAG-2746-2021
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'
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times-cited: '140'
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title: Toward understanding the impact of artificial intelligence on labor
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type: Article
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unique-id: WOS:000463069900008
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usage-count-last-180-days: '92'
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usage-count-since-2013: '443'
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volume: '116'
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web-of-science-categories: Multidisciplinary Sciences
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year: '2019'
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