Global Counterfactual Explanations: Investigations, Implementations and Improvements
This work addresses the need for reliable and computationally tractable global explanations in explainability, particularly for recourse applications, but it appears incremental as it builds on an existing framework.
The paper tackles the problem of generating global counterfactual explanations, which are lacking in existing methods, by investigating and improving the Actionable Recourse Summaries (AReS) framework to provide more efficient and interactive tools for practitioners.
Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding. However, the major shortcoming associated with these methods is their inability to provide explanations beyond the local or instance-level. While some 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 either reliable or computationally tractable. Meanwhile, practitioners are requesting more efficient and interactive explainability tools. We take this opportunity to investigate existing global methods, with a focus on implementing and improving Actionable Recourse Summaries (AReS), the only known global counterfactual explanation framework for recourse.