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
type: Article
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'