  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.
