RobustX: robust counterfactual explanations made
easy
easy
File(s)
Accepted version
Author(s)
Jiang, Jay
Marzari, Luca
Purohit, Aaryan
Leofante, Francesco
Type
Conference Paper
Abstract
The increasing use of Machine Learning (ML) models to aid decision-making in high-stakes industries demands explainability to facilitate trust. Counterfactual Explanations (CEs) are ideally suited for this, as they can offer insights into the predictions of an ML model by illustrating how changes in its input data may lead to different outcomes. However, for CEs to realise their explanatory potential, significant challenges remain in ensuring their robustness under slight changes in the scenario being explained. Despite the widespread recognition of CEs’ robustness as a fundamental requirement, a lack of standardised tools and benchmarks hinders a comprehensive and effective comparison of robust CE generation methods. In this paper, we introduce RobustX, an open-source Python library implementing a collection of CE generation and evaluation methods, with a focus on the robustness property. RobustX provides inter-
faces to several existing methods from the literature, enabling streamlined access to state-of-the-art techniques. The library is also easily extensible, allowing fast prototyping of novel robust CE generation and evaluation methods.
faces to several existing methods from the literature, enabling streamlined access to state-of-the-art techniques. The library is also easily extensible, allowing fast prototyping of novel robust CE generation and evaluation methods.
Date Issued
2025-08-16
Date Acceptance
2025-04-29
Citation
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 2025, pp.11067-11071
ISBN
978-1-956792-06-5
Publisher
IJCAI
Start Page
11067
End Page
11071
Journal / Book Title
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Copyright Statement
Copyright © 2025 IJCAI. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
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
Date Publish Online
2025-08-16