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首先，With the rise of AI agents, Shao et al. [124] introduce an auditing framework that studies which occupational tasks workers prefer AI agents to automate or augment, incorporating an audio-enhanced mini-interview protocol and proposing the Human Agency Scale to quantify desired levels of human involvement. The accompanying WORKBank database includes responses from 1500 domain works covering 844 tasks spanning 104 occupations. Rinberg et al. [125] develop RippleBench to analyze how updates to AI systems propagate, examining second-order effects of model updates.
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