人工智能参与对专家咨询工作流中信任的影响
Asymmetric Trust Effects of Corrective AI in Expert Advisory Workflows under Epistemic Dependence
AI总结：
研究探讨人工智能在专家咨询工作流中对用户信任的影响，发现人类专家使用AI的方式会影响用户对专业知识的信任。
摘要：
随着人工智能驱动工具在医学、法律和金融等专家工作流中的日益整合，一个关键问题浮现：人工智能的参与如何影响用户对人类专家、人工智能系统及其组合的信任？为了探讨这一问题，我们进行了一项用户研究（N=77），涉及模拟的课程规划任务。我们比较了不同条件，这些条件在AI的存在与否以及人类与AI协作的具体模式上有所不同。我们的结果表明，虽然顾问创建正确日程的能力很重要，但用户对专业知识和信任的感知也受到专家如何利用AI助手的影响。这些发现对设计人类-人工智能混合团队具有重要考虑，特别是在采用建议依赖于终端用户对推荐者专业知识的感知时。
英文摘要：
The increasing integration of AI-powered tools into expert workflows, such as medicine, law, and finance, raises a critical question: how does AI involvement influence a user`s trust in the human expert, the AI system, and the human-AI team? This question is especially important in expert advisory settings where users are epistemically dependent on human-AI systems: they are recipients of guidance produced by an expert using AI support, but often lack the domain knowledge needed to independently verify the recommendation. We investigated these dynamics through a user study (N=157) using a simulated course-planning task. Our design varied advisor performance and the structure of AI involvement, including whether AI support was present and, when present, whether it was invoked by the advisor or automatically monitored the interaction. Across all conditions, workflows ultimately produced correct schedules. Results show an asymmetric trust effect: advisor errors significantly reduce trust in the human advisor, but visible AI correction does not produce a corresponding increase in trust toward the AI assistant. Trust judgments remain anchored to the advisor across multiple trust measures, and changing the visible structure of AI involvement does not substantially redistribute trust toward the AI assistant or the human-AI team. These findings suggest a limit of corrective AI as a governance mechanism: making AI oversight visible may improve workflow recoverability, but it does not necessarily redistribute trust or responsibility away from the human-facing expert. In epistemically dependent settings, correctness alone may be insufficient for trustworthy AI integration, as users may continue to assign responsibility to the human expert even when AI assistance visibly shapes the final outcome.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
