为智能环境设计可操作的反事实解释，帮用户知道如何改进结果。
From Facts to Foils: Designing and Evaluating Counterfactual Explanations for Smart Environments
- 提出首个专用于规则型智能环境的反事实解释生成方法
- 用户研究显示：问题解决时更偏好反事实解释，紧急情况则倾向简单因果解释
- 适合需要具体改进建议的智能系统开发者和用户体验设计者
可解释性正成为基于规则的智能环境的关键特性。尽管反事实解释（说明若采取不同行为可达成理想结果）是可解释AI中的有力工具，但在这类规则型领域尚无成熟生成方法。本文首次形式化并实现了一种面向该领域的反事实解释框架，作为现有解释引擎的插件。通过17名用户的实验评估，发现用户偏好高度依赖情境：在时间紧迫时更偏好语言简洁的因果解释；而在希望解决问题时，更青睐提供具体行动建议的反事实解释。本工作为智能环境提供了新型解释的实用框架，并实证支持了不同类型解释的适用场景选择。
原文摘要 · Abstract (English)
Explainability is increasingly seen as an essential feature of rule-based smart environments. While counterfactual explanations, which describe what could have been done differently to achieve a desired outcome, are a powerful tool in eXplainable AI (XAI), no established methods exist for generating them in these rule-based domains. In this paper, we present the first formalization and implementation of counterfactual explanations tailored to this domain. It is implemented as a plugin that extends an existing explanation engine for smart environments. We conducted a user study (N=17) to evaluate our generated counterfactuals against traditional causal explanations. The results show that user preference is highly contextual: causal explanations are favored for their linguistic simplicity and in time-pressured situations, while counterfactuals are preferred for their actionable content, particularly when a user wants to resolve a problem. Our work contributes a practical framework for a new type of explanation in smart environments and provides empirical evidence to guide the choice of when each explanation type is most effective.
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