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Counterfactual explanations have been widely studied in explainability, witha range of application dependent methods prominent in fairness, recourse andmodel understanding. The major shortcoming associated with these methods,however, is their inability to provide explanations beyond the local orinstance-level. While many works touch upon the notion of a global explanation,typically suggesting to aggregate masses of local explanations in the hope ofascertaining global properties, few provide frameworks that are both reliableand computationally tractable. Meanwhile, practitioners are requesting moreefficient and interactive explainability tools. We take this opportunity topropose Global & Efficient Counterfactual Explanations (GLOBE-CE), a flexibleframework that tackles the reliability and scalability issues associated withcurrent state-of-the-art, particularly on higher dimensional datasets and inthe presence of continuous features. Furthermore, we provide a uniquemathematical analysis of categorical feature translations, utilising it in ourmethod. Experimental evaluation with publicly available datasets and userstudies demonstrate that GLOBE-CE performs significantly better than thecurrent state-of-the-art across multiple metrics (e.g., speed, reliability).

GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations