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
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 maximize interpretability. The primary challenge, therefore, is to balance these trade-offs—maximizing effectiveness, minimizing cost, while maintaining a small number of actions. We introduce GLANCE, a versatile and adaptive algorithm that employs a novel agglomerative approach, jointly considering both the feature space and the space of counterfactual actions, thereby accounting for the distribution of points in a way that aligns with the model
BibTeX
@inproceedings{aaai2026_glanceglobalacti,
title = {GLANCE: Global Actions in a Nutshell for Counterfactual Explainability},
author = {Loukas Kavouras and Eleni Psaroudaki and Konstantinos Tsopelas and Dimitrios Rontogiannis and Nikolaos Theologitis and Dimitris Sacharidis and Giorgos Giannopoulos and Dimitrios Tomaras and Kleopatra Markou and Dimitrios Gunopulos and Dimitris Fotakis and Ioannis Emiris},
booktitle = {AAAI 2026},
year = {2026}
}