In this section
@inproceedings{Jiang:2025:ijcai.2025/1264,
author = {Jiang, J and Marzari, L and Purohit, A and Leofante, F},
doi = {ijcai.2025/1264},
pages = {11067--11071},
publisher = {IJCAI},
title = {RobustX: robust counterfactual explanations madeeasy},
url = {http://dx.doi.org/10.24963/ijcai.2025/1264},
year = {2025}
}
TY - CPAPER
AB - 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.
AU - Jiang,J
AU - Marzari,L
AU - Purohit,A
AU - Leofante,F
DO - ijcai.2025/1264
EP - 11071
PB - IJCAI
PY - 2025///
SP - 11067
TI - RobustX: robust counterfactual explanations madeeasy
UR - http://dx.doi.org/10.24963/ijcai.2025/1264
ER -
