Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: The paper presents a feature attribution method based on information bottleneck attribution (IBA). While IBA has shown to be an effective method for feature attribution, they are a few limitations such as, variational approximation. The authors mentioned that for lower layers of the network, the variational approximation causes an over-estimation of mutual information which leads to misleading attribution results. The authors proposed a bottleneck variable that keeps the deep features with prediction information and search for input that corresponds to the features in deep layers. 

A thorough experimentation is conducted comparing vision and NLP models using various attribution methods belonging to a diverse set of classes. Quantitative results show that their method works better or on-par with the other attribution based methods.
main_review: The paper proposes an extension to information bottleneck attribution. Their proposed method is simple. However, it shows promising results when compared against several alternate methods.

Questions: 
- Figure 2 shows full face of the object as important feature. I am wondering if the proposed method covers more area of the input? In other words, have a better recall in highlighting input features?
- Table 1: the results of IBA and integrated gradient are extremely bad. Is there a difference of scale of the attribution value which is causing IBA to highlight everything? For integrated gradient, what is the baseline? The confidence of the prediction score also have an effect on the input feature attribution. Did author look at them to make sure the they are running other methods with the optimal or reasonable settings?

limitations_and_societal_impact: Yes, authors have done a great job in addressing the limitations of their approach.
review_title: null

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: This paper introduces a new interpretability method, building on top of the Information Bottleneck Attribution [IBA] literature, by ensuring that the predictive information of the input features is extracted directly, rather than through approximation as in IBA. This method has the advantage that no variational approximation is needed for the input probability distribution, meaning that the interpolation to the input dimension is also not needed. The contribution of this method is that it provides accurate info about the interpretation offered by the input features due to lack of overapproximation. The paper also contains detailed experiments comparing multiple interpretation methods in the literature, using varied metrics and also passes the sanity check for sensitivity to parameter randomization. 
main_review: The paper is well structured, and gives thorough background and motivation for the problem. It also contains plenty of literature review, allowing readers to understand the progression of the story of interpretability relatively well. The contributions are solid, and the experimental results demonstrate superiority of this method over IBA, and similarity to past methods by other metrics. Questions: 

1. Line 124, why is it reasonable to take the optimal result for Q(Z) as opposed to the average result? Can the paper list the disadvantages of taking the optimal result vs the average result? 
2. Line 152 is the first time local explainability is mentioned as a goal. I would expect that to be mentioned earlier in the paper when different interpretation methods are being compared in the literature search, and especially comment on whether different methods are local vs global and put the current work and results in context of the usefulness due to the global vs local nature. 
3. While it is commendable that the paper is comparing work on two tasks -- NLP and vision, it does not seem fair to compare interpretation methods specifically created in the Image domain (GradCAM, Integrated Gradients etc not LIME) when applied to NLP. There should be commentary provided on which methods rely on the spatial correlation depicted in images, to allow the readers to fairly compare the interpretation methods in Vision and NLP. There should also be commentary on the advantages and disadvantages different methods depict when crossing domains. 
4. Section 4.2, it would be helpful to see multiple qualitative egs, and also a rough estimate of the number of egs where the paper’s method seemed to outperform. It will also be helpful to know how the examples were selected, to ensure that no cherry picking was involved. 
5. In Section 4.4, it will be helpful to have information on the advantages/ disadvantages of the different metrics applied, and what it means to evaluate the number of examples considered for each. 
6. I find it odd that the dataset was suddenly changed in 4.4.3, due to computational limitations. I would expect CIFAR evaluation for other metrics as well, if this was necessary. 

limitations_and_societal_impact: The paper somewhat sufficiently addresses the societal impact. However, towards the end of the paper, it is important and helpful to have commentary on the advantages of different interpretation methods (including the one proposed in this paper), so that future work has decent categorization of interpretation methods useful for different tasks.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: Authors build up on a recent attribution technique (IBA), identifies shortcomings and proposes a new technique that utilizes a measure of information for input features (vs latent). New method does not have architecture restrictions.

They evaluate their method with other types of explanability techniques in a set of image and NLP tasks and propose an additional task/metric with "effective heat ratio" (EHR).

main_review: Work is novel, clear, and further the field of XAI. The work is somewhat original (building heavily on IBA). Experiments are extensive enough for image and text data. I am also glad the author have done sanity checks on their technique. I rate the work a 6 ("Marginally above the acceptance threshold") and think with a few edits it can be a 7 or 8.

Authors refer to their method as "Ours" or as "Λ", authors might want to consider a name for their method so that future work can more easily reference it (InputBA?).

The authors mention in line 310 that mask estimation is a new idea to the attribution community. This might be overclaimed since GNN explainability techniques will often figure out a mask for the inputs [1].

The hyperparameters of an explainability technique have a strong influence on their performance [2], authors might want to expand on the hyperparameters of "their" technique.

Work could be improved by including other data modalities (tabular data, or graph data [4]) and evaluating them quantitatively [4-8]. In particular, the lens of explanation qualities might be useful to talk about failure modes or strengths of different explainability techniques ([3]).
 
Confidence Intervals or statistical tests on numerical results would strengthen claims.


Citations: 
[1] https://arxiv.org/abs/1903.03894
[2] https://arxiv.org/abs/2003.08754
[3] https://link.springer.com/chapter/10.1007/978-3-319-90403-0_9
[4] https://papers.nips.cc/paper/2020/hash/417fbbf2e9d5a28a855a11894b2e795a-Abstract.html 
[5] https://arxiv.org/abs/1904.11829 
[6] https://arxiv.org/abs/2003.07258 
[7] https://arxiv.org/abs/1806.10758 
[8] https://arxiv.org/abs/1806.10758
limitations_and_societal_impact: Adequately addressed.
review_title: null

Meta-review: The reviewers are all in consensus that the paper is worthy of acceptance.

I want to thank the authors for their extensive responses to the reviews.  It appears as though the reviews will benefit the paper and lead to improvements for the final form.
Decision: Accept (Poster)