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：摘要：反事实解释在可解释性领域已被广泛研究，在公平性、补救机制和模型理解等应用中涌现出多种任务依赖的方法。然而，这些方法的主要缺陷在于其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推测全局属性，但鲜有框架能兼具可靠性与计算可行性。与此同时，实践者正呼吁更高效且可交互的可解释性工具。为此，我们提出全局高效反事实解释（GLOBE-CE）这一灵活框架，旨在解决当前最先进方法在可靠性及可扩展性方面的问题，特别是在高维数据集及连续特征场景下。此外，我们首次对分类特征变换进行数学分析，并将其应用于该方法中。基于公开数据集的实验评估与用户研究表明，GLOBE-CE在速度、可靠性等多个指标上显著优于当前最先进方法。