Leofante, Francesco, Botoeva, Elena, Rajani, Vineet (2023) Counterfactual explanations and model multiplicity: a relational verification view. In: Proceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning. . pp. 763-768. IJCAI ISBN 978-1-956792-02-7. (doi:10.24963/kr.2023/78) (KAR id:112768)
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| Official URL: https://doi.org/10.24963/kr.2023/78 |
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Abstract
We study the interplay between counterfactual explanations and model multiplicity in the context of neural network classifiers. We show that current explanation methods often produce counterfactuals whose validity is not preserved under model multiplicity. We then study the problem of generating counterfactuals that are guaranteed to be robust to model multiplicity, characterise its complexity and propose an approach to solve this problem using ideas from relational verification.
| Item Type: | Conference or workshop item (Proceeding) |
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| DOI/Identification number: | 10.24963/kr.2023/78 |
| Uncontrolled keywords: | counterfactual explanations, model multiplicity, neural networks, explainable AI |
| Subjects: | Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science |
| Institutional Unit: | Schools > School of Computing |
| Former Institutional Unit: |
There are no former institutional units.
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| Funders: | University of Kent (https://ror.org/00xkeyj56) |
| Depositing User: | Elena Botoeva |
| Date Deposited: | 16 Jan 2026 18:09 UTC |
| Last Modified: | 21 Jan 2026 03:46 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/112768 (The current URI for this page, for reference purposes) |
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https://orcid.org/0000-0001-5881-0258
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