Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This work attends to the problem of “leakage” in the soft concept bottleneck models (CBMs): the soft concepts can convey unintentional information about the label, which compromises the interpretability and intervenability of a CBM -- the main purpose of such models -- even if it may improve the label prediction accuracy. The paper suggests two techniques: (1) a side channel to learn latent concepts to complement the insufficient (known) concepts under the Markovian assumption and (2) an autoregressive model to learn correlation among concepts. The authors conduct experiments on MIMIC-III and CUB-2011 to show the effectiveness of their proposed approaches including whether indeed adding the side-channel improves the label prediction and adding the autoregressive model improves the concept prediction accuracy.

strengths_and_weaknesses: Strengths:
* The main idea and presentation of the paper is good overall.
* The experimental results on the autoregressive model (Hard AR) performing well on the concept prediction as well as the label prediction (also when intervened) are nice.
* The adaptive use of the side-channel from the amortization network shows its applicability for different scenarios.

Weaknesses:
* Perhaps it’s unfair but to me the pitch of this work (and thus the title) is a bit misleading: this paper is actually focused on closing the gap between soft and hard CBMs (in terms of the performance) rather than directly addressing the leakage issue in soft CBMs. To elaborate, adding a side channel does not prevent leakage; it helps improve predictive performance of a hard CBM which already/inherently prevents the leakage issue. If you say the leakage issue is being addressed, one would expect something like improving based upon a soft CBM, not hard one. Perhaps simply “improving hard CBMs” for example or “Addressing Two Shortcomings of Concept Bottleneck Models (the title of Section 4)” would suit better.
* Modeling correlations between concepts doesn’t quite fit the main motivation of preventing leakage; also, it’s not convincing yet why modeling correlations between concepts should be a key.
* The paper is not addressing potential limitations of the proposed approach at all, except they say it’s costly for a large data set at the end of Section 5. I think this part needs a lot more addressing on the computational overhead (for amortization network, autoregressive model, MC sampling, etc.) to make this work fair.
* There is a potential room for improvements in modeling the correlations among concepts. However, the proposed idea of using an autoregressive model seems quite under-explored.
* The two main ideas of using the side channel and autoregressive model are separate.
* The leakage is already addressed in prior work [Mahinpei et al., 2021, Margeloiu et al. 2021] and therefore the demonstration of it itself doesn’t seem to add much new insights.

questions: * Am I correct in understanding that the authors did not provide any result on a model that employs both side channel and autoregressive models altogether?
* Autoregressive modeling for concept prediction seems quite random and only adds for improving predictive performance. How is it relevant to addressing the leakage issue?
* It’s not clearly explained why there is a need for the path from c to z and how to train this amortization network `gamma`.

limitations: * There are a few places that appear to be written at the last minute and especially the main technical parts are not described entirely clearly.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper considers the problem of leakage for concept bottleneck models (CBM). CBMs are models that predict concepts from features and then predict labels from concepts. When concept probabilities contain information about the label, leakage happens, which decreases intervention performance: when users intervene by changing concepts. Hard CBMs use binary concepts and avoid leakage but have lower performance. This paper proposes two methods for improving the performance of hard CBMs: (1) autoregressive where a concept is predicted using features and all previous concepts, (2) side channel where the label is predicted from concepts and directly from features. In experiments, the authors found that (1) autoregressive improves performance, especially intervention performance when a few concepts are changed, (2) side-channel improves performance when a few concepts are available.

strengths_and_weaknesses: Strengths:
- CBMs are important models that support intervention. Leakage is a major issue with CBMs.
- Experimental results are mostly positive.

Weaknesses:
- Clarity: while I understand the paper now, I did get confused on my first read. The many ideas being presented got a bit mixed up. I think a reorganization will improve the clarity of the paper. 

- Novelty: The application of autoregressive and side channeling to CBMs is novel, but these are fairly common modeling techniques, so the novelty is not high.

- Experiment 1 showed that autoregressive hard CBMs have better intervention performance when a small number of concepts (e.g. 5 out of 20) are changed. I think this was because the changed concepts propagate to other concepts, so it only works well for changing concepts that are around the beginning. This may also make it more difficult for users: changing one concept makes other concepts change. I think these should be tested in experiments and discussed.

- Experiment 2 showed that side channeling improved hard CBM when a small number of concepts are available. With a side channel, the model can predict the label directly from features, so this improvement is not surprising. The original CBM paper discussed side channeling as a potential future work, but pointed out a drawback: "cannot cleanly intervene on a single concept". So I think this means the intervention performance will be lower. This should also be tested in experiments.
questions: See my suggestions in the weaknesses section above.
limitations: The authors have adequately addressed the limitations.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: The authors present a novel concept bottleneck model (CBM) aimed at
retaining the interpretability and suitability to be combined with
human interventions of hard CBMs (as compared to soft CBM), while
addressing two major limitations of hard CBM that they identify as
potential causes affecting their predictive accuracy: the inability to
deal with correlated concepts, and the problem of having an incomplete
concept set.
strengths_and_weaknesses: The work is definitely relevant, given the importance that
interpretable neural networks and concept-based models have in
boosting trustworthiness and reliability of deep networks. The
proposed architecture is novel and all contributions are appropriately
motivated, with each contribution addressing one of the shortcomings
of existing hard CBMs. 

The paper is sound in terms of justifications for the different
components introduced (the side channel, the autoregressive model) and
the strategies used for training, prediction and for dealing with
intervention, as well as for the introduction of the information
theoretical completeness score.
questions: How crucial is the marginalization on concepts for the performance in
hard CBM? how would an approach maximizing on concepts rather than
marginalizing perform? 
limitations: The authors highlight the increased training cost due to the
autoregressive and side-channel components, which is however not
dramatic. One issue that would be worth discussing is the impact on
interpretability of the marginalization on concepts that is performed
on hard CBM, and how the marginalization on side concepts further affects it.
review_title: null

Meta-review: metareview: The paper contains an interesting discussion on Concept Bottleneck Models pointing out their main flaws. The proposed remedies are plausible and documented empirically. The reviewers have converged to the consensus and all have scored the paper above the bar. 

Minor comments:
- The caption of Figure 1 seems to be wrong. The middle part is swapped with the right part.
- It might be worth to say that the concept learning is a multi-label classification problem. The last method resembles classifier chains (particularly probabilistic classifier chains). 
confidence: Certain
Decision: Accept