Robust counterfactual explanations in machine learning: a survey
File(s)
Published version
Author(s)
Jiang, Junqi
Leofante, Francesco
Rago, Antonio
Toni, Francesca
Type
Conference Paper
Abstract
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.
Date Issued
2024-08-03
Date Acceptance
2024-04-20
Citation
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024, pp.8086-8094
Publisher
International Joint Conferences on Artificial Intelligence Organization (IJCAI)
Start Page
8086
End Page
8094
Journal / Book Title
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Copyright Statement
© 2024 International Joint Conferences on Artificial Intelligence.
Sponsor
JPMorgan Chase Bank, N.A.
Commission of the European Communities
Royal Academy Of Engineering
Grant Number
COLAR_P86244
101020934
RCSRF2021\11\45
Source
The 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
Publication Status
Published
Start Date
2024-08-03
Finish Date
2024-08-09
Coverage Spatial
Jeju, Korea