GLANCE: Global Actions in a Nutshell for Counterfactual Explainability
Loukas Kavouras, Eleni Psaroudaki, Konstantinos Tsopelas, Dimitrios Rontogiannis, Nikolaos Theologitis, Dimitris Sacharidis, Giorgos Giannopoulos, Dimitrios Tomaras, Kleopatra Markou, Dimitrios Gunopulos, Dimitris Fotakis, Ioannis Emiris

TL;DR
GLANCE is an adaptive algorithm for global counterfactual explanations that balances effectiveness, cost, and interpretability by considering data distribution and model structure.
Contribution
It introduces a novel agglomerative approach that jointly considers feature and action spaces for improved counterfactual explanations.
Findings
GLANCE outperforms existing methods in robustness and performance.
It effectively balances trade-offs among effectiveness, cost, and interpretability.
Experimental results show consistent improvements across datasets and models.
Abstract
The widespread deployment of machine learning systems in critical real-world decision-making applications has highlighted the urgent need for counterfactual explainability methods that operate effectively. Global counterfactual explanations, expressed as actions to offer recourse, aim to provide succinct explanations and insights applicable to large population subgroups. High effectiveness, measured by the fraction of the population that is provided recourse, ensures that the actions benefit as many individuals as possible. Keeping the cost of actions low ensures the proposed recourse actions remain practical and actionable. Limiting the number of actions that provide global counterfactuals is essential to maximizing interpretability. The primary challenge, therefore, is to balance these trade-offs--maximizing effectiveness, minimizing cost, while maintaining a small number of actions.…
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