Delegation and Verification Under AI
Lingxiao Huang,
Wenyang Xiao and
Nisheeth Vishnoi
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Lingxiao Huang: Nanjing University
Wenyang Xiao: Nanjing University
Nisheeth Vishnoi: Yale University
No 2500, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University
Abstract:
As AI systems enter institutional workflows, workers must decide whether to delegate task execution to AI and how much effort to invest in verifying AI outputs, while institutions evaluate workers using outcome-based standards that may misalign with workers' private costs. We model delegation and verification as the solution to a rational worker's optimization problem, and define worker quality by evaluating an institution-centered utility (distinct from the worker's objective) at the resulting optimal action. We formally characterize optimal worker workflows and show that AI induces phase transitions, where arbitrarily small differences in verification ability lead to sharply different behaviors. As a result, AI can amplify workers with strong verification reliability while degrading institutional worker quality for others who rationally over-delegate and reduce oversight, even when baseline task success improves and no behavioral biases are present. These results identify a structural mechanism by which AI reshapes institutional worker quality and amplifies quality disparities between workers with different verification reliability.
Pages: 35 pages
Date: 2026-03-01
New Economics Papers: this item is included in nep-mic
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