研究如何用解释提升用户对AI的信任，尤其关注不确定性表达。
Uncertainty Awareness and Trust in Explainable AI- On Trust Calibration using Local and Global Explanations
- 结合局部与全局解释，校准用户对AI决策的信任度。
- 复杂但直观的可视化解释能显著提升用户满意度。
- 提出一套可推广的XAI信任校准框架，适合人机交互研究者。
可解释人工智能（XAI）已成为学术界广泛关注的主题，受到计算机科学家、统计学家以及心理学和哲学研究者的审视。尽管部分领域已有深入研究，本文聚焦于不确定性解释，并考虑常被忽视的全局解释。我们采用一种涵盖不确定性、鲁棒性及全局XAI概念的算法，测试其在信任校准方面的效果。同时，评估一种虽复杂但具直观视觉表现的算法是否能带来更高的用户满意度与人类可解释性。研究结果表明，该算法在提升信任感知方面具有潜力，为构建更可信的XAI系统提供了实用指导。
原文摘要 · Abstract (English)
Explainable AI has become a common term in the literature, scrutinized by computer scientists and statisticians and highlighted by psychological or philosophical researchers. One major effort many researchers tackle is constructing general guidelines for XAI schemes, which we derived from our study. While some areas of XAI are well studied, we focus on uncertainty explanations and consider global explanations, which are often left out. We chose an algorithm that covers various concepts simultaneously, such as uncertainty, robustness, and global XAI, and tested its ability to calibrate trust. We then checked whether an algorithm that aims to provide more of an intuitive visual understanding, despite being complicated to understand, can provide higher user satisfaction and human interpretability.
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