Toward understanding the impact of artificial intelligence on labor

Morgan R. Frank, David Autor, James E. Bessen, Erik Brynjolfsson, Manuel Cebrian, David J. Deming, Maryann Feldman, Matthew Groh, José Lobo, Esteban Moro, Dashun Wang, Hyejin Youn, Iyad Rahwan*

*Corresponding author for this work

Research output: Contribution to journalReview article

3 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)6531-6539
Number of pages9
JournalProceedings of the National Academy of Sciences of the United States of America
Volume116
Issue number14
DOIs
StatePublished - Apr 2 2019

Fingerprint

Artificial Intelligence
Automation
Occupations
Economics
Technology
Unemployment
Emigration and Immigration
Workplace
Uncertainty
Fear
Research

Keywords

  • Automation
  • Economic resilience
  • Employment
  • Future of work

ASJC Scopus subject areas

  • General

Cite this

Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., ... Rahwan, I. (2019). Toward understanding the impact of artificial intelligence on labor. Proceedings of the National Academy of Sciences of the United States of America, 116(14), 6531-6539. https://doi.org/10.1073/pnas.1900949116
Frank, Morgan R. ; Autor, David ; Bessen, James E. ; Brynjolfsson, Erik ; Cebrian, Manuel ; Deming, David J. ; Feldman, Maryann ; Groh, Matthew ; Lobo, José ; Moro, Esteban ; Wang, Dashun ; Youn, Hyejin ; Rahwan, Iyad. / Toward understanding the impact of artificial intelligence on labor. In: Proceedings of the National Academy of Sciences of the United States of America. 2019 ; Vol. 116, No. 14. pp. 6531-6539.
@article{7d7ba036741b450db676caf3ece41eac,
title = "Toward understanding the impact of artificial intelligence on labor",
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, Economic resilience, Employment, Future of work",
author = "Frank, {Morgan R.} and David Autor and Bessen, {James E.} and Erik Brynjolfsson and Manuel Cebrian and Deming, {David J.} and Maryann Feldman and Matthew Groh and Jos{\'e} Lobo and Esteban Moro and Dashun Wang and Hyejin Youn and Iyad Rahwan",
year = "2019",
month = "4",
day = "2",
doi = "10.1073/pnas.1900949116",
language = "English (US)",
volume = "116",
pages = "6531--6539",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
number = "14",

}

Frank, MR, Autor, D, Bessen, JE, Brynjolfsson, E, Cebrian, M, Deming, DJ, Feldman, M, Groh, M, Lobo, J, Moro, E, Wang, D, Youn, H & Rahwan, I 2019, 'Toward understanding the impact of artificial intelligence on labor', Proceedings of the National Academy of Sciences of the United States of America, vol. 116, no. 14, pp. 6531-6539. https://doi.org/10.1073/pnas.1900949116

Toward understanding the impact of artificial intelligence on labor. / Frank, Morgan R.; Autor, David; Bessen, James E.; Brynjolfsson, Erik; Cebrian, Manuel; Deming, David J.; Feldman, Maryann; Groh, Matthew; Lobo, José; Moro, Esteban; Wang, Dashun; Youn, Hyejin; Rahwan, Iyad.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 116, No. 14, 02.04.2019, p. 6531-6539.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Toward understanding the impact of artificial intelligence on labor

AU - Frank, Morgan R.

AU - Autor, David

AU - Bessen, James E.

AU - Brynjolfsson, Erik

AU - Cebrian, Manuel

AU - Deming, David J.

AU - Feldman, Maryann

AU - Groh, Matthew

AU - Lobo, José

AU - Moro, Esteban

AU - Wang, Dashun

AU - Youn, Hyejin

AU - Rahwan, Iyad

PY - 2019/4/2

Y1 - 2019/4/2

N2 - 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.

AB - 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.

KW - Automation

KW - Economic resilience

KW - Employment

KW - Future of work

UR - http://www.scopus.com/inward/record.url?scp=85064058602&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85064058602&partnerID=8YFLogxK

U2 - 10.1073/pnas.1900949116

DO - 10.1073/pnas.1900949116

M3 - Review article

C2 - 30910965

AN - SCOPUS:85064058602

VL - 116

SP - 6531

EP - 6539

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

SN - 0027-8424

IS - 14

ER -