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).

提出了一种名为 GLOBE-CE 的灵活框架，旨在解决当前解释性方法中与可靠性和可扩展性有关的问题，并在针对高维数据集和连续特征存在的情况下提供全局和高效的反事实解释，通过公开可用的数据集和用户研究进行了评估，证明了该框架在速度、可靠性等多个指标上明显优于当前先进技术。