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The Allocation of Decision Authority to Human and Artificial Intelligence

Author

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  • Susan C. Athey
  • Kevin A. Bryan
  • Joshua S. Gans

Abstract

The allocation of decision authority by a principal to either a human agent or an artificial intelligence (AI) is examined. The principal trades off an AI's more aligned choice with the need to motivate the human agent to expend effort in learning choice payoffs. When agent effort is desired, it is shown that the principal is more likely to give that agent decision authority, reduce investment in AI reliability, and adopt an AI that may be biased. Organizational design considerations are likely to have an impact on how AIs are trained.

Suggested Citation

  • Susan C. Athey & Kevin A. Bryan & Joshua S. Gans, 2020. "The Allocation of Decision Authority to Human and Artificial Intelligence," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 80-84, May.
    • Handle: RePEc:aea:apandp:v:110:y:2020:p:80-84
    DOI: 10.1257/pandp.20201034
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    References listed on IDEAS

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    1. 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.
    2. Agrawal, Ajay & Gans, Joshua S. & Goldfarb, Avi, 2019. "Exploring the impact of artificial Intelligence: Prediction versus judgment," Information Economics and Policy, Elsevier, vol. 47(C), pages 1-6.
    3. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1, March.
    4. Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338, December.
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    Cited by:

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    9. Bauer, Kevin & von Zahn, Moritz & Hinz, Oliver, 2023. "Please take over: XAI, delegation of authority, and domain knowledge," SAFE Working Paper Series 394, Leibniz Institute for Financial Research SAFE.
    10. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    11. Marie Obidzinski & Yves Oytana, 2022. "Prediction, human decision and liability rules, CRED Working paper No 2022-06," Working Papers hal-04034871, HAL.
    12. Laura Blattner & Scott Nelson & Jann Spiess, 2021. "Unpacking the Black Box: Regulating Algorithmic Decisions," Papers 2110.03443, arXiv.org, revised Jul 2023.
    13. Caro-Burnett, Johann & Kaneko, Shinji, 2022. "Is Society Ready for AI Ethical Decision Making? Lessons from a Study on Autonomous Cars," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 98(C).
    14. Talia Gillis & Bryce McLaughlin & Jann Spiess, 2021. "On the Fairness of Machine-Assisted Human Decisions," Papers 2110.15310, arXiv.org, revised Sep 2023.

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    More about this item

    JEL classification:

    • D23 - Microeconomics - - Production and Organizations - - - Organizational Behavior; Transaction Costs; Property Rights
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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