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Exploring the Impact of Artificial Intelligence: Prediction versus Judgment

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  • Ajay K. Agrawal
  • Joshua S. Gans
  • Avi Goldfarb

Abstract

Based on recent developments in the field of artificial intelligence (AI), we examine what type of human labor will be a substitute versus a complement to emerging technologies. We argue that these recent developments reduce the costs of providing a particular set of tasks – prediction tasks. Prediction about uncertain states of the world is an input into decision-making. We show that prediction allows riskier decisions to be taken and this is its impact on observed productivity although it could also increase the variance of outcomes as well. We consider the role of human judgment in decision-making as prediction technology improves. Judgment is exercised when the objective function for a particular set of decisions cannot be described (i.e., coded). However, we demonstrate that better prediction impacts the returns to different types of judgment in opposite ways. Hence, not all human judgment will be a complement to AI. Finally, we show that humans will delegate some decisions to machines even when the decision would be superior with human input.

Suggested Citation

  • Ajay K. Agrawal & Joshua S. Gans & Avi Goldfarb, 2018. "Exploring the Impact of Artificial Intelligence: Prediction versus Judgment," NBER Working Papers 24626, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:24626
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    References listed on IDEAS

    as
    1. Ben S. Bernanke, 1983. "Irreversibility, Uncertainty, and Cyclical Investment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 98(1), pages 85-106.
    2. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2018. "Human Judgment and AI Pricing," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 58-63, May.
    3. Aghion, Philippe & Tirole, Jean, 1997. "Formal and Real Authority in Organizations," Journal of Political Economy, University of Chicago Press, vol. 105(1), pages 1-29, February.
    4. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation," NBER Working Papers 24449, National Bureau of Economic Research, Inc.
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    6. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    7. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 115-146, National Bureau of Economic Research, Inc.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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