预测反事实解释满意度：基于人类对解释质量评分的分析
Predicting Satisfaction of Counterfactual Explanations from Human Ratings of Explanatory Qualities
AI总结：
本研究通过206名参与者对反事实解释的评分，分析七个解释质量维度，发现可行性和信任度是满意度的最强预测因子，且其他指标可解释58%方差，为算法设计提供指导。
摘要：
反事实解释是可解释人工智能中广泛使用的一种方法，通过说明对输入数据的微小改变如何导致不同结果，为决策提供可操作的见解。尽管其重要性不言而喻，但评估反事实解释的质量仍是一个开放问题。传统的定量指标（如稀疏性或邻近性）无法完全反映人类对解释的偏好，而用户研究虽有洞察力但难以规模化。此外，仅依赖单一的整体满意度评分无法深入理解某些解释有效或无效的原因。为解决这一问题，我们分析了一个由206名人类参与者评估的反事实解释数据集，他们不仅对整体满意度进行评分，还评定了七个解释标准：可行性、连贯性、复杂性、可理解性、完整性、公平性和信任度。将整体满意度建模为这些标准的函数，我们发现可行性（建议改变的可操作性）和信任度（相信改变会导致预期结果的信念）始终是用户满意度最强的预测因子，尽管完整性也作为一个有意义的贡献因素出现。关键的是，即使排除可行性和信任度，其他指标也能解释58%的方差，凸显了额外解释质量的重要性。复杂性似乎独立存在，表明更详细的解释不一定降低满意度。强指标相关性暗示了用户评判质量时存在潜在结构，且人口统计学背景显著影响排名模式。这些见解为设计反事实算法提供了信息，使其能够根据用户专业知识和领域背景调整解释质量。
英文摘要：
Counterfactual explanations are a widely used approach in Explainable AI, offering actionable insights into decision-making by illustrating how small changes to input data can lead to different outcomes. Despite their importance, evaluating the quality of counterfactual explanations remains an open problem. Traditional quantitative metrics, such as sparsity or proximity, fail to fully account for human preferences in explanations, while user studies are insightful but not scalable. Moreover, relying only on a single overall satisfaction rating does not lead to a nuanced understanding of why certain explanations are effective or not. To address this, we analyze a dataset of counterfactual explanations that were evaluated by 206 human participants, who rated not only overall satisfaction but also seven explanatory criteria: feasibility, coherence, complexity, understandability, completeness, fairness, and trust. Modeling overall satisfaction as a function of these criteria, we find that feasibility (the actionability of suggested changes) and trust (the belief that the changes would lead to the desired outcome) consistently stand out as the strongest predictors of user satisfaction, though completeness also emerges as a meaningful contributor. Crucially, even excluding feasibility and trust, other metrics explain 58% of the variance, highlighting the importance of additional explanatory qualities. Complexity appears independent, suggesting more detailed explanations do not necessarily reduce satisfaction. Strong metric correlations imply a latent structure in how users judge quality, and demographic background significantly shapes ranking patterns. These insights inform the design of counterfactual algorithms that adapt explanatory qualities to user expertise and domain context.
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。
