Robust Counterfactual Explanations in Machine Learning: A Survey
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916113107910656 |
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| author | Jiang, Junqi Leofante, Francesco Rago, Antonio Toni, Francesca |
| author_facet | Jiang, Junqi Leofante, Francesco Rago, Antonio Toni, Francesca |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01928 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust Counterfactual Explanations in Machine Learning: A Survey Jiang, Junqi Leofante, Francesco Rago, Antonio Toni, Francesca Machine Learning Artificial Intelligence 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. |
| title | Robust Counterfactual Explanations in Machine Learning: A Survey |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.01928 |