wow-inequalities/02-data/intermediate/wos_sample/f208623eb8454572c655535193be8d4f-frank-morgan-r.-and/info.yaml

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2023-09-28 14:46:10 +00:00
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.'
affiliation: 'Rahwan, I (Corresponding Author), MIT, Media Lab, Cambridge, MA 02139
USA.
Rahwan, I (Corresponding Author), MIT, Inst Data Syst \& Soc, 77 Massachusetts Ave,
Cambridge, MA 02139 USA.
Rahwan, I (Corresponding Author), Max Planck Inst Human Dev, Ctr Humans \& Machines,
D-14195 Berlin, Germany.
Frank, Morgan R.; Cebrian, Manuel; Groh, Matthew; Moro, Esteban; Rahwan, Iyad, MIT,
Media Lab, Cambridge, MA 02139 USA.
Autor, David, MIT, Dept Econ, Cambridge, MA 02139 USA.
Bessen, James E., Boston Univ, Sch Law, Technol \& Policy Res Initiat, Boston, MA
02215 USA.
Brynjolfsson, Erik, MIT, Sloan Sch Management, 77 Massachusetts Ave, Cambridge,
MA 02139 USA.
Brynjolfsson, Erik, Natl Bur Econ Res, Cambridge, MA 02138 USA.
Deming, David J., Harvard Univ, Harvard Kennedy Sch, Cambridge, MA 02138 USA.
Deming, David J., Harvard Univ, Grad Sch Educ, Cambridge, MA 02138 USA.
Feldman, Maryann, Univ N Carolina, Dept Publ Policy, Chapel Hill, NC 27599 USA.
Lobo, Jose, Arizona State Univ, Sch Sustainabil, Tempe, AZ 85287 USA.
Moro, Esteban, Univ Carlos III Madrid, Escuela Politecn Super, Dept Matemat, Grp
Interdisciplinar Sistemas Complejos, Madrid 28911, Spain.
Wang, Dashun; Youn, Hyejin, Northwestern Univ, Kellogg Sch Management, Evanston,
IL 60208 USA.
Wang, Dashun; Youn, Hyejin, Northwestern Univ, Northwestern Inst Complex Syst, Evanston,
IL 60208 USA.
Rahwan, Iyad, MIT, Inst Data Syst \& Soc, 77 Massachusetts Ave, Cambridge, MA 02139
USA.
Rahwan, Iyad, Max Planck Inst Human Dev, Ctr Humans \& Machines, D-14195 Berlin,
Germany.'
author: Frank, Morgan R. and Autor, David and Bessen, James E. and Brynjolfsson, Erik
and Cebrian, Manuel and Deming, David J. and Feldman, Maryann and Groh, Matthew
and Lobo, Jose and Moro, Esteban and Wang, Dashun and Youn, Hyejin and Rahwan, Iyad
author-email: irahwan@mit.edu
author_list:
- family: Frank
given: Morgan R.
- family: Autor
given: David
- family: Bessen
given: James E.
- family: Brynjolfsson
given: Erik
- family: Cebrian
given: Manuel
- family: Deming
given: David J.
- family: Feldman
given: Maryann
- family: Groh
given: Matthew
- family: Lobo
given: Jose
- family: Moro
given: Esteban
- family: Wang
given: Dashun
- family: Youn
given: Hyejin
- family: Rahwan
given: Iyad
da: '2023-09-28'
doi: 10.1073/pnas.1900949116
eissn: 1091-6490
esi-highly-cited-paper: Y
esi-hot-paper: N
files: []
issn: 0027-8424
journal: 'PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF
AMERICA'
keywords: automation; employment; economic resilience; future of work
keywords-plus: SKILL; FUTURE; TASKS; JOBS; PROFESSION; EMPLOYMENT; DEMANDS; GROWTH
language: English
month: APR 2
number: '14'
number-of-cited-references: '85'
orcid-numbers: 'Rahwan, Iyad/0000-0002-1796-4303
Moro, Esteban/0000-0003-2894-1024
Youn, Hyejin/0000-0002-6190-4412
Lobo, Jose/0000-0002-0814-7168
/0000-0001-9487-9359
/0000-0002-6915-9381
Groh, Matthew/0000-0002-9029-0157'
pages: 6531-6539
papis_id: 6be6fb5f2bb6a333ec3e47263a7895e5
ref: Frank2019understandingimpact
researcherid-numbers: 'Rahwan, Iyad/ABB-2422-2020
Frank, Morgan R/L-3124-2016
Moro, Esteban/AAB-1159-2019
Youn, Hyejin/ABD-2997-2020
Lobo, Jose/AAG-2746-2021
'
times-cited: '140'
title: Toward understanding the impact of artificial intelligence on labor
2023-10-01 08:15:07 +00:00
type: article
2023-09-28 14:46:10 +00:00
unique-id: WOS:000463069900008
usage-count-last-180-days: '92'
usage-count-since-2013: '443'
volume: '116'
web-of-science-categories: Multidisciplinary Sciences
year: '2019'