The major shortcoming associated with counterfactual methods is their inability to provide explanations beyond the local or instance-level. 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.

The major shortcoming associated with counterfactual methods is their inability to provide explanations beyond the local or instance-level. While some works touch upon the notion of a global explanation, 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.

To interpret uncertainty estimates, we extend recent work that generates multiple Counterfactual Latent Uncertainty Explanations (𝛿-CLUEs), by applying additional constraints for diversity in the optimisation objective (∇-CLUE). We then propose a distinct method for discovering GLobal AMortised CLUEs (GLAM-CLUE) which learns mappings of arbitrary complexity between groups of uncertain and certain groups in a computationally efficient manner.

To interpret uncertainty estimates, we extend recent work that generates Counterfactual Latent Uncertainty Explanations (CLUEs), to produce a set of plausible CLUEs- multiple, diverse inputs that are within a δ ball of the original input in latent space, all yielding confident predictions.