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The Wrong Kind of AI? Artificial Intelligence and the Future of Labor Demand

Author

Listed:
  • Acemoglu, Daron

    (MIT)

  • Restrepo, Pascual

    (Boston University)

Abstract

Artificial Intelligence is set to influence every aspect of our lives, not least the way production is organized. AI, as a technology platform, can automate tasks previously performed by labor or create new tasks and activities in which humans can be productively employed. Recent technological change has been biased towards automation, with insufficient focus on creating new tasks where labor can be productively employed. The consequences of this choice have been stagnating labor demand, declining labor share in national income, rising inequality and lower productivity growth. The current tendency is to develop AI in the direction of further automation, but this might mean missing out on the promise of the "right" kind of AI with better economic and social outcomes.

Suggested Citation

  • Acemoglu, Daron & Restrepo, Pascual, 2019. "The Wrong Kind of AI? Artificial Intelligence and the Future of Labor Demand," IZA Discussion Papers 12292, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp12292
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    References listed on IDEAS

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    1. The Wrong Kind of AI? Artificial Intelligence and the Future of Labor Demand
      by maximorossi in NEP-LTV blog on 2019-05-14 14:30:41

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    Cited by:

    1. Ballestar, María Teresa & Díaz-Chao, Ángel & Sainz, Jorge & Torrent-Sellens, Joan, 2021. "Impact of robotics on manufacturing: A longitudinal machine learning perspective," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. Liu, Liang & Yang, Kun & Fujii, Hidemichi & Liu, Jun, 2021. "Artificial Intelligence and Energy Intensity in China’s Industrial Sector: Effect and Transmission Channel," MPRA Paper 106333, University Library of Munich, Germany.
    3. Luigi Marengo, 2019. "Is this time different? A note on automation and labour in the fourth industrial revolution," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 46(3), pages 323-331, September.
    4. Nippani, Abishek, 2020. "Automation and Labour in India: Policy Implications of Job Polarisation pre and post COVID-19 crisis," SocArXiv h9gaw, Center for Open Science.
    5. Mutascu, Mihai, 2021. "Artificial intelligence and unemployment: New insights," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 653-667.
    6. Atheendar S Venkataramani & Rourke O’Brien & Gregory L Whitehorn & Alexander C Tsai, 2020. "Economic influences on population health in the United States: Toward policymaking driven by data and evidence," PLOS Medicine, Public Library of Science, vol. 17(9), pages 1-17, September.
    7. Judith Clifton & Amy Glasmeier & Mia Gray, 2020. "When machines think for us: the consequences for work and place," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 3-23.
    8. Xie, Mengmeng & Ding, Lin & Xia, Yan & Guo, Jianfeng & Pan, Jiaofeng & Wang, Huijuan, 2021. "Does artificial intelligence affect the pattern of skill demand? Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 96(C), pages 295-309.
    9. Anton Korinek, 2019. "Integrating Ethical Values and Economic Value to Steer Progress in Artificial Intelligence," NBER Working Papers 26130, National Bureau of Economic Research, Inc.

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    More about this item

    Keywords

    automation; artificial intelligence; jobs; inequality; innovation; labor demand; productivity; tasks; technology; wages;
    All these keywords.

    JEL classification:

    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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