员工怕用AI显得没自信，影响工作效率。
Barriers to AI Adoption: Image Concerns at Work
- 通过在线实验发现，员工可见依赖AI时更少采纳建议。
- 可见依赖导致任务表现下降15%以上，且无法通过历史记录缓解。
- 员工担忧用AI暴露不自信，适合关注人机协作心理的读者。
关于员工如何被评价的顾虑会阻碍与人工智能（AI）的有效协作。在大型在线劳动力市场上开展的一项实地实验中，我雇佣了450名美国远程工作者完成一项由AI推荐辅助的图像分类任务。工作绩效将决定是否获得合同续签，由人力资源评估员根据反馈评分。研究发现，当员工对AI的依赖程度可被评估员察觉时，他们采纳AI建议的比例显著降低，导致任务表现明显下滑。这一效应在设计保守的前提下依然存在：员工明确知晓评估员被指示基于同一AI辅助任务的预期准确率进行评价。即使评估员得知员工平台历史表现优异，这种对AI的回避行为仍持续存在，凸显此类顾虑难以消除。借助平台公开反馈功能，本文提出一种新型激励相容的引出方法，揭示员工担忧过度依赖AI会传递出缺乏自信的信号，而这种自信被视为与AI协作的关键特质。
原文摘要 · Abstract (English)
Concerns about how workers are perceived can deter effective collaboration with artificial intelligence (AI). In a field experiment on a large online labor market, I hired 450 U.S.-based remote workers to complete an image-categorization job assisted by AI recommendations. Workers were incentivized by the prospect of a contract extension based on an HR evaluator's feedback. I find that workers adopt AI recommendations at lower rates when their reliance on AI is visible to the evaluator, resulting in a measurable decline in task performance. The effects are present despite a conservative design in which workers know that the evaluator is explicitly instructed to assess expected accuracy on the same AI-assisted task. This reduction in AI reliance persists even when the evaluator is reassured about workers' strong performance history on the platform, underscoring how difficult these concerns are to alleviate. Leveraging the platform's public feedback feature, I introduce a novel incentive-compatible elicitation method showing that workers fear heavy reliance on AI signals a lack of confidence in their own judgment, a trait they view as essential when collaborating with AI.
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