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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
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    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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