Summary
The paper focuses on the robustness of explanations. It begins by defining explanation invariance and equivariance concepts using geometric deep learning formalism and demonstrates that certain popular interpretability methods inherently possess theoretical robustness guarantees. Two metrics, invariance and equivariance scores, are introduced for empirically assessing explanation robustness. These metrics are applied to evaluate various interpretability methods across different modalities. Finally, the paper provides a set of five actionable guidelines to ensure that interpretability methods are employed in a manner that guarantees robustness.
Strengths
1. The paper presents a high-level framework for evaluating the robustness of explanations and introduces two corresponding metrics. Unlike previous work that mostly focuses on saliency-based explanations for image classification, this framework can be applied to various explanation methods (feature-based, concept-based, and example-based) and modalities (images, graphs, and time series);
2. In addition to offering an evaluation framework, the authors also provide guidelines for generating robust explanations. These insights can assist the community in developing improved explanation methods.
Weaknesses
1. The paper's organization could be better aligned with the summaries of contributions provided in the abstract and on page 3. The structure in later sections does not closely follow these summaries, which may make it difficult for readers to follow the narrative.
2. The paper appears to cover many points, potentially leading to the omission of important details.
I am interested in understanding how different explanation methods relate to theoretical robustness guarantees (invariant, equivariant), but the paper only presents the results (Table 1) without discussing them. Although the mathematical proofs are available in Appendix D, the main paper lacks an explanation and discussion of these results. For instance, while Table 1 indicates that gradient-based methods have conditional equivariance guarantees, the necessary conditions or assumptions are not explicitly stated. Including explanations or discussions in the main paper would significantly improve clarity.
3. The use of the Dihedral Group for CIFAR10 and STL10 in the experimental section is unclear. It would be helpful if the authors provided an example of the transformations applied to the images in this context.
Questions
1. Could the authors provide further discussion and explanations regarding the results in Table 1, especially for those that are conditionally guaranteed?
2. In the experimental section, what does the Dihedral Group represent for CIFAR10 and STL10? Could the authors share an example of the specific transformations applied to images in this scenario?
3. While there are numerous publications on the explanations for NLP, the paper does not mention the robustness of explanations for NLP. Is it possible to apply the proposed framework to evaluate the robustness of these methods within the NLP domain?
Rating
5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
Confidence
4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Limitations
See weaknesses.
In general, I think the paper tackles an important research question in XAI and proposes a valuable framework to address the issue. However, the concerns mentioned in the "weaknesses" section, such as missing or unclear information, may lead to confusion and make it difficult for readers to fully understand the paper.
If these related questions can be well answered, I may consider raising my rating.