Emotion-sensitive Explanation Model（情绪敏感型解释模型）
Emotion-sensitive Explanation Model
AI总结：
针对情绪因素会影响用户对XAI解释的理解这一问题，提出情绪敏感解释的三阶段模型，为开发适配用户情绪的动态解释XAI系统提供概念基础，助力更有效的以用户为中心的决策。
摘要：
可解释人工智能（XAI）研究传统上聚焦于理性用户，旨在提升理解并减少认知偏差。然而，情绪因素在解释被感知和处理的方式中起着关键作用。已有研究表明，预先存在的情绪和任务产生的情绪会对解释的理解产生负面影响。基于这些发现，我们提出了一个用于情绪敏感解释基础的三阶段模型：（1）情绪或认知唤醒，（2）理解，（3）认同。该模型为开发能根据用户情绪状态动态调整解释策略的XAI系统提供了概念基础，最终支持更有效、以用户为中心的决策。
英文摘要：
Explainable AI (XAI) research has traditionally focused on rational users, aiming to improve understanding and reduce cognitive biases. However, emotional factors play a critical role in how explanations are perceived and processed. Prior work shows that prior and task-generated emotions can negatively impact the understanding of explanation. Building on these insights, we propose a three-stage model for emotion-sensitive explanation grounding: (1) emotional or epistemic arousal, (2) understanding, and (3) agreement. This model provides a conceptual basis for developing XAI systems that dynamically adapt explanation strategies to users emotional states, ultimately supporting more effective and user-centered decision-making.
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。
