Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
This addresses the need for reliable and interpretable explanations in AI systems, particularly for users and regulators, though it is incremental by improving upon existing robustness methods.
The paper tackles the problem that counterfactual explanations for neural networks are easily invalidated by model updates and may be implausible outliers, proposing PROPLACE to generate explanations that are provably robust and plausible, achieving state-of-the-art performance in comparative experiments.
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.