In this section
@inproceedings{Jiang:2024:ijcai.2024/894,
author = {Jiang, J and Leofante, F and Rago, A and Toni, F},
doi = {ijcai.2024/894},
pages = {8086--8094},
publisher = {International Joint Conferences on Artificial Intelligence Organization (IJCAI)},
title = {Robust counterfactual explanations in machine learning: a survey},
url = {http://dx.doi.org/10.24963/ijcai.2024/894},
year = {2024}
}
TY - CPAPER
AB - Counterfactual explanations (CEs) are advocated as being ideally suited to providing algorithmic recourse for subjects affected by the predictions of machine learning models. While CEs can be beneficial to affected individuals, recent work has exposed severe issues related to the robustness of state-of-the-art methods for obtaining CEs. Since a lack of robustness may compromise the validity of CEs, techniques to mitigate this risk are in order. In this survey, we review works in the rapidly growing area of robust CEs and perform an in-depth analysis of the forms of robustness they consider. We also discuss existing solutions and their limitations, providing a solid foundation for future developments.
AU - Jiang,J
AU - Leofante,F
AU - Rago,A
AU - Toni,F
DO - ijcai.2024/894
EP - 8094
PB - International Joint Conferences on Artificial Intelligence Organization (IJCAI)
PY - 2024///
SP - 8086
TI - Robust counterfactual explanations in machine learning: a survey
UR - http://dx.doi.org/10.24963/ijcai.2024/894
ER -
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