透明度陷阱：AI免责声明如何在高风险决策中引发过度自信
The Transparency Trap: How AI Disclaimers Create Overconfidence in High-Stakes Decisions
- Amador Valley（阿马多尔谷高中）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过跨三个高风险领域的实验，揭示AI免责声明存在透明度悖论，会引发用户过度自信，为负责任AI设计等提供关键启示。
AI中文摘要：
当前AI免责声明往往因警告习惯化和透明度悖论而无法发挥预期作用。随着AI生成信息在日常决策中变得普遍，有效的风险沟通对于负责任设计愈发关键。本探索性研究考察了免责声明的位置和说服性线索如何在金融、医学和AI生成内容三个高风险领域中影响信任、感知准确性和免责声明参与度。采用混合被试内-被试间实验设计，共收集52名参与者的378个刺激水平响应。研究发现，在所有条件下，建议内容普遍受到信任，即使存在免责声明。显著的领域效应显示，医学内容获得最高信任评级。在AI领域，研究结果揭示了透明度悖论：部分参与者将免责声明解读为系统自我意识和诚实的标志，而非警告，反而悖论性地提升了感知可信度。横幅盲症的证据进一步表明，标准化AI免责声明不足以防止过度依赖。金融和医学作为有用的比较领域，展现了用户如何根据情境和感知风险不同地解读警告。这些发现对负责任AI设计、算法公平性以及用户在高风险情境中依据潜在误导性信息行事时的消费者保护具有重要意义。
英文摘要：
Current AI disclaimers often fail to function as intended due to warning habituation and a transparency paradox. As AI-generated information becomes pervasive in everyday decision-making, effective risk communication is increasingly critical for responsible design. This exploratory study examines how disclaimer placement and persuasive cues shape trust, perceived accuracy, and disclaimer engagement across three high-stakes domains: finance, medicine, and AI-generated content. Using a mixed within-between experimental design with 378 stimulus-level responses from 52 participants, we find that advisory content was generally trusted across conditions, even when disclaimers were present. A significant domain effect showed that medical content received the highest trust ratings. In the AI domain, the findings reveal a transparency paradox: some participants interpreted disclaimers not as warnings, but as signs of system self-awareness and honesty, paradoxically increasing perceived trustworthiness. Evidence of banner blindness further suggests that standardized AI disclaimers are insufficient to prevent over-reliance. Finance and medicine provide useful comparison domains by showing how users interpret warnings differently depending on context and perceived risk. These findings have vital implications for responsible AI design, algorithmic fairness, and consumer protection when users act on potentially misleading information in high-stakes settings.
