AI让员工决策是否交由机器执行任务，验证能力差异会导致行为剧变。
Delegation and Verification Under AI
- 基于理性优化建模员工的委托与验证策略
- 微小验证能力差异引发行为突变，导致工作质量两极分化
- 适合关注人机协作中质量不均问题的研究者
随着AI进入制度化工作流程，员工需决定是否将任务委托给AI，并投入多少精力验证其输出，而机构则依据结果评估员工，这可能与员工私有成本不一致。本文将委托与验证建模为理性员工的优化问题，通过机构中心效用定义员工质量（不同于员工自身目标）。我们形式化刻画了最优工作流程，发现AI会引发相变：验证能力的微小差异导致截然不同的行为。结果表明，即便基础任务成功率提升且无行为偏差，AI仍会放大验证能力强的员工表现，同时使其他员工因过度委托、减少监督而降低机构评价下的工作质量。该机制揭示了AI如何结构性重塑组织内员工质量，并加剧不同验证可靠性员工之间的差距。
原文摘要 · Abstract (English)
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.
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