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The Allocation of Decision Authority to Human and Artificial Intelligence

Author

Listed:
  • Athey, Susan

    (Stanford U)

  • Bryan, Kevin

    (U of Toronto)

  • Gans, Joshua S.

    (U of Toronto)

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 impact on how AIs are trained.

Suggested Citation

  • Athey, Susan & Bryan, Kevin & Gans, Joshua S., 2020. "The Allocation of Decision Authority to Human and Artificial Intelligence," Research Papers 3856, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:3856
    as

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    References listed on IDEAS

    as
    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. 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.
    3. repec:ner:ucllon:http://discovery.ucl.ac.uk/17678/ is not listed on IDEAS
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Bryce McLaughlin & Jann Spiess, 2022. "Algorithmic Assistance with Recommendation-Dependent Preferences," Papers 2208.07626, arXiv.org, revised Jan 2024.
    2. Lyu, Wenjing & Liu, Jin, 2021. "Soft skills, hard skills: What matters most? Evidence from job postings," Applied Energy, Elsevier, vol. 300(C).
    3. Jonathan Gruber & Benjamin R. Handel & Samuel H. Kina & Jonathan T. Kolstad, 2020. "Managing Intelligence: Skilled Experts and AI in Markets for Complex Products," NBER Working Papers 27038, National Bureau of Economic Research, Inc.
    4. Laura Abrardi & Carlo Cambini & Laura Rondi, 2022. "Artificial intelligence, firms and consumer behavior: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 969-991, September.
    5. Laura Blattner & Scott Nelson & Jann Spiess, 2021. "Unpacking the Black Box: Regulating Algorithmic Decisions," Papers 2110.03443, arXiv.org, revised Jul 2023.
    6. Marie Obidzinski & Yves Oytana, 2022. "Advisory algorithms and liability rules," Working Papers hal-04222291, HAL.
    7. Talia Gillis & Bryce McLaughlin & Jann Spiess, 2021. "On the Fairness of Machine-Assisted Human Decisions," Papers 2110.15310, arXiv.org, revised Sep 2023.
    8. Antonio Rodríguez Andrés & Voxi Heinrich S. Amavilah & Abraham Otero, 2021. "Evaluation of technology clubs by clustering: a cautionary note," Applied Economics, Taylor & Francis Journals, vol. 53(52), pages 5989-6001, November.
    9. Ashesh Rambachan & Jon Kleinberg & Sendhil Mullainathan & Jens Ludwig, 2020. "An Economic Approach to Regulating Algorithms," NBER Working Papers 27111, National Bureau of Economic Research, Inc.
    10. Feldhaus, Christoph & Lingens, Jörg & Löschel, Andreas & Zunker, Gerald, 2022. "Encouraging consumer activity through automatic switching of the electricity contract - A field experiment," Energy Policy, Elsevier, vol. 164(C).
    11. 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.
    12. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    13. Marie Obidzinski & Yves Oytana, 2022. "Prediction, human decision and liability rules, CRED Working paper No 2022-06," Working Papers hal-04034871, HAL.
    14. 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).

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    More about this item

    JEL classification:

    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • M54 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Labor Management
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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