错误解释会误导人类，反而降低人机协作效率。
Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration
- 用错误解释误导人类，即使AI建议正确
- 导致人类形成错误推理策略，影响后续任务
- 适合关注人机协作设计的从业者参考
在多种应用场景中，人类越来越多地使用缺乏透明度的黑箱AI系统。为应对这一问题，可解释人工智能（XAI）方法承诺提升透明度与可解释性。尽管已有研究探讨了XAI对人机协作的影响，但极少关注错误解释带来的潜在危害。本研究（n=160）通过实验考察了人机协同决策中XAI的作用。结果发现，当错误解释伴随正确AI建议时，会产生误导效应，导致人类推断出错误的推理策略，损害任务执行能力，并表现出程序性知识受损。此外，错误解释还会削弱人机团队在协作过程中的整体表现。本研究为人机交互（HCI）领域提供了实证证据，揭示了错误解释对人类的负面影响，并为AI系统设计者提出指导原则。
原文摘要 · Abstract (English)
Across various applications, humans increasingly use black-box artificial intelligence (AI) systems without insight into these systems' reasoning. To counter this opacity, explainable AI (XAI) methods promise enhanced transparency and interpretability. While recent studies have explored how XAI affects human-AI collaboration, few have examined the potential pitfalls caused by incorrect explanations. The implications for humans can be far-reaching but have not been explored extensively. To investigate this, we ran a study (n=160) on AI-assisted decision-making in which humans were supported by XAI. Our findings reveal a misinformation effect when incorrect explanations accompany correct AI advice with implications post-collaboration. This effect causes humans to infer flawed reasoning strategies, hindering task execution and demonstrating impaired procedural knowledge. Additionally, incorrect explanations compromise human-AI team-performance during collaboration. With our work, we contribute to HCI by providing empirical evidence for the negative consequences of incorrect explanations on humans post-collaboration and outlining guidelines for designers of AI.
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