Counterfactual explanations under model multiplicity and their use in computational argumentation
File(s)
Author(s)
Alfano, Gianvincenzo
Gould, Adam
Leofante, Francesco
Rago, Antonio
Toni, Francesca
Type
Conference Paper
Abstract
Counterfactual explanations (CXs) are widely recognised as an essential technique for providing recourse recommendations for AI models. However, it is not obvious how to determine CXs in model multiplicity scenarios, where equally performing but different models can be obtained for the same task. In this paper, we propose novel qualitative and quantitative definitions of CXs based on explicit, nested quantification over (groups) of model decisions. We also study properties of these notions and identify decision problems of interest therefor. While our CXs are broadly applicable, in this paper we instantiate them within computational argumentation where model multiplicity naturally emerges, e.g. with incomplete and case-based argumentation frameworks. We then illustrate the suitability of our CXs for model multiplicity in legal and healthcare contexts, before analysing the complexity of the associated decision problems.
Date Issued
2025-08-01
Date Acceptance
2025-04-29
Citation
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 2025, pp.4321-4329
Publisher
IJCAI
Start Page
4321
End Page
4329
Journal / Book Title
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Copyright Statement
Subject to copyright. This paper is embargoed until publication.
Source
International Joint Conference on Artificial Intelligence (IJCAI) 2025
Publication Status
Published
Start Date
2025-08-16
Finish Date
2025-08-22
Coverage Spatial
Montreal, Canada