Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation

Junqi Jiang, Jianglin Lan, Francesco Leofante, Antonio Rago, Francesca Toni
Proceedings of the 15th Asian Conference on Machine Learning, PMLR 222:582-597, 2024.

Abstract

Counterfactual Explanations (CEs) have received increasing interest as a major methodology for explaining neural network classifiers. Usually, CEs for an input-output pair are defined as data points with minimum distance to the input that are classified with a different label than the output. To tackle the established problem that CEs are easily invalidated when model parameters are updated (e.g. retrained), studies have proposed ways to certify the robustness of CEs under model parameter changes bounded by a norm ball. However, existing methods targeting this form of robustness are not sound or complete, and they may generate implausible CEs, i.e., outliers wrt the training dataset. In fact, no existing method simultaneously optimises for closeness and plausibility while preserving robustness guarantees. In this work, we propose Provably RObust and PLAusible Counterfactual Explanations (PROPLACE), a method leveraging on robust optimisation techniques to address the aforementioned limitations in the literature. We formulate an iterative algorithm to compute provably robust CEs and prove its convergence, soundness and completeness. Through a comparative experiment involving six baselines, five of which target robustness, we show that PROPLACE achieves state-of-the-art performances against metrics on three evaluation aspects.

Cite this Paper

BibTeX
@InProceedings{pmlr-v222-jiang24a, title = {Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation}, author = {Jiang, Junqi and Lan, Jianglin and Leofante, Francesco and Rago, Antonio and Toni, Francesca}, booktitle = {Proceedings of the 15th Asian Conference on Machine Learning}, pages = {582--597}, year = {2024}, editor = {Yanıkoğlu, Berrin and Buntine, Wray}, volume = {222}, series = {Proceedings of Machine Learning Research}, month = {11--14 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v222/jiang24a/jiang24a.pdf}, url = {https://proceedings.mlr.press/v222/jiang24a.html}, abstract = {Counterfactual Explanations (CEs) have received increasing interest as a major methodology for explaining neural network classifiers. Usually, CEs for an input-output pair are defined as data points with minimum distance to the input that are classified with a different label than the output. To tackle the established problem that CEs are easily invalidated when model parameters are updated (e.g. retrained), studies have proposed ways to certify the robustness of CEs under model parameter changes bounded by a norm ball. However, existing methods targeting this form of robustness are not sound or complete, and they may generate implausible CEs, i.e., outliers wrt the training dataset. In fact, no existing method simultaneously optimises for closeness and plausibility while preserving robustness guarantees. In this work, we propose Provably RObust and PLAusible Counterfactual Explanations (PROPLACE), a method leveraging on robust optimisation techniques to address the aforementioned limitations in the literature. We formulate an iterative algorithm to compute provably robust CEs and prove its convergence, soundness and completeness. Through a comparative experiment involving six baselines, five of which target robustness, we show that PROPLACE achieves state-of-the-art performances against metrics on three evaluation aspects.} }
Endnote
%0 Conference Paper %T Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation %A Junqi Jiang %A Jianglin Lan %A Francesco Leofante %A Antonio Rago %A Francesca Toni %B Proceedings of the 15th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Berrin Yanıkoğlu %E Wray Buntine %F pmlr-v222-jiang24a %I PMLR %P 582--597 %U https://proceedings.mlr.press/v222/jiang24a.html %V 222 %X Counterfactual Explanations (CEs) have received increasing interest as a major methodology for explaining neural network classifiers. Usually, CEs for an input-output pair are defined as data points with minimum distance to the input that are classified with a different label than the output. To tackle the established problem that CEs are easily invalidated when model parameters are updated (e.g. retrained), studies have proposed ways to certify the robustness of CEs under model parameter changes bounded by a norm ball. However, existing methods targeting this form of robustness are not sound or complete, and they may generate implausible CEs, i.e., outliers wrt the training dataset. In fact, no existing method simultaneously optimises for closeness and plausibility while preserving robustness guarantees. In this work, we propose Provably RObust and PLAusible Counterfactual Explanations (PROPLACE), a method leveraging on robust optimisation techniques to address the aforementioned limitations in the literature. We formulate an iterative algorithm to compute provably robust CEs and prove its convergence, soundness and completeness. Through a comparative experiment involving six baselines, five of which target robustness, we show that PROPLACE achieves state-of-the-art performances against metrics on three evaluation aspects.
APA
Jiang, J., Lan, J., Leofante, F., Rago, A. & Toni, F.. (2024). Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation. Proceedings of the 15th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 222:582-597 Available from https://proceedings.mlr.press/v222/jiang24a.html.

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