引入基于用户反馈的反事实解释（UFCE）
Introducing User Feedback-based Counterfactual Explanations (UFCE)
AI总结：
针对现有反事实解释忽视关键特征与建议实用性的问题，提出UFCE方法，通过纳入用户约束和特征依赖生成最小可行修改，并在五个数据集上验证其在邻近性、稀疏性和可行性上优于两种主流方法。
摘要：
机器学习模型被广泛应用于现实世界的应用中。然而，其复杂性往往使得解释其决策背后的依据具有挑战性。在可解释人工智能（XAI）中，反事实解释（CE）已成为生成可理解解释的一种可行解决方案。CE向用户提供可操作的信息，说明如何通过对输入进行最小修改来获得期望结果。然而，当前的CE算法在优化改变以扭转非期望结果时，通常在整个特征空间内运行，忽视了对结果关键促成因素的识别，也忽视了所建议改变的实用性。在本研究中，我们提出一种名为基于用户反馈的反事实解释（UFCE）的新方法，以解决这些局限，并旨在增强人们对所提供解释的信心。UFCE允许纳入用户约束，在考虑特征依赖性的同时，确定可操作特征子集中的最小修改，并使用基准评估指标评估所建议改变的实用性。我们使用五个数据集开展了三项实验，结果表明UFCE在邻近性、稀疏性和可行性方面优于两种知名CE方法。报告结果表明，用户约束会影响可行CE的生成。
英文摘要：
Machine learning models are widely used in real-world applications. However, their complexity makes it often challenging to interpret the rationale behind their decisions. Counterfactual explanations (CEs) have emerged as a viable solution for generating comprehensible explanations in eXplainable Artificial Intelligence (XAI). CE provides actionable information to users on how to achieve the desired outcome with minimal modifications to the input. However, current CE algorithms usually operate within the entire feature space when optimizing changes to turn over an undesired outcome, overlooking the identification of key contributors to the outcome and disregarding the practicality of the suggested changes. In this study, we introduce a novel methodology, that is named as user feedback-based counterfactual explanation (UFCE), which addresses these limitations and aims to bolster confidence in the provided explanations. UFCE allows for the inclusion of user constraints to determine the smallest modifications in the subset of actionable features while considering feature dependence, and evaluates the practicality of suggested changes using benchmark evaluation metrics. We conducted three experiments with five datasets, demonstrating that UFCE outperforms two well-known CE methods in terms of \textit{proximity}, \textit{sparsity}, and \textit{feasibility}. Reported results indicate that user constraints influence the generation of feasible CEs.
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。
