Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods prominent in fairness, recourse and model understanding. The major shortcoming associated with these methods, however, is their inability to provide explanations beyond the local or instance-level. While many works touch upon the notion of a global explanation, typically suggesting to aggregate masses of local explanations in the hope of ascertaining global properties, few provide frameworks that are both reliable and computationally tractable. Meanwhile, practitioners are requesting more efficient and interactive explainability tools. We take this opportunity to propose Global & Efficient Counterfactual Explanations (GLOBE-CE), a flexible framework that tackles the reliability and scalability issues associated with current state-of-the-art, particularly on higher dimensional datasets and in the presence of continuous features. Furthermore, we provide a unique mathematical analysis of categorical feature translations, utilising it in our method. Experimental evaluation with publicly available datasets and user studies demonstrate that GLOBE-CE performs significantly better than the current state-of-the-art across multiple metrics (e.g., speed, reliability).

翻译：反事实解释在可解释性领域已被广泛研究，其中一系列依赖于应用的方法在公平性、可回溯性及模型理解方面表现突出。然而，这些方法的主要缺陷在于无法提供超越局部或实例级别的解释。尽管许多研究涉及全局解释的概念（通常建议通过聚合大量局部解释来获取全局属性），但鲜有框架兼具可靠性与计算可行性。与此同时，实践者正迫切需求更高效、更具交互性的可解释性工具。我们借此契机提出GLOBE-CE（全局与高效反事实解释），该灵活框架解决了当前最先进方法在处理高维数据集及连续特征时存在的可靠性与可扩展性问题。此外，我们提出了一种针对分类特征翻译的独特数学分析方法，并将其应用于本方法中。基于公开数据集及用户研究的实验评估表明，GLOBE-CE在速度、可靠性等多维度指标上均显著优于当前最先进方法。