reviews: 
Comments: This paper addresses the problem of explanation disagreement (or explanatory multiplicity), where (post-hoc) explanations of a given machine learning model conflicts with one another. The authors propose a new framework called EXAGREE to find a model, stakeholder-aligned explanation model (SAEM), that provides explanations (feature attribution rankings) in accordance with stakeholder desiderata.
Strengths: The technical framework of end-to-end optimization problem which involves constructing the Rashomon set, DMAN, sorting networks and multi-heads architecture is interesting.
Weaknesses: However, this paper should be rejected because:
(1) it is built on weak understanding of explainability
(2) there is a weak connection between "explanation disagreement" and the solution
(3) has questionable experiment design and metrics without sufficient justification
(4) it is unrefined

My biggest concern is that the paper uses local explanatory models (i.e. LIME, Integrated Gradients) to generate global (model-level) explanations. In Section 2.3, the authors mention that they have adapted feature attribution methods for local explanations "by averaging feature attributions across all instances to obtain global attributions." These methods were not designed to be used this way. Although SHAP does have functionality to provide model-level feature attribution, it takes an average of the **absolute** attribution across instances. 

My impression of the paper's proposed solution, EXAGREE, is that it attempts to address "explanation disagreement" by finding a model that aligns with stakeholder expectations (i.e., based on domain knowledge) through examining its post-hoc explanation. There is one critical assumption here: the post-hoc explanation is faithful to model behavior --- something we cannot take for granted (see e.g. [Adebayo et al. (2019)](https://arxiv.org/abs/1810.03292)). Besides, I don't think the solution addresses the problem of "explanation disagreement", but is rather a model-selection tool using post-hoc explanations. I see that there are two cases of "explanation disagreement" (both of which is mentioned in the paper):
1. Models with similar performance give different explanations (explanation method fixed)
2. Explanation methods provide different explanations for one model (model fixed)
EXAGREE addresses 1 to an extent but not 2 --- the paper does not make this clear. The authors seem to suggest that the "stakeholder centric" approach can address complex disagreements (Section 2.2). But I don't see how it addresses case 2.

Moreover, I am not convinced that "higher agreement between rankings implies greater faithfulness". Bad actors might want explanations that hide the discriminatory nature of their models, hence want features to be ranked a certain way. In fact, several works have highlighted that explainability methods are prone to manipulation:  [Slack et al. (2020)](https://arxiv.org/abs/1911.02508), [Aivodji et al. (2019)](https://proceedings.mlr.press/v97/aivodji19a.html), [Goethals et al. (2023)](https://arxiv.org/abs/2306.13885). Explainability methods are tools to gain insight into a model (to potentially build trust) not project our desired belief upon the model.

As a result, the metrics, which are based on an unsubstantiated assumption that agreement $\implies$ faithfulness, and the empirical results fall short of achieving the goals outlined in the introduction: to identify models "that provide fair faithful and trustworthy explanations."

The experimental design uses the "ground truth" explanation, the coefficients of the LR model, as "stakeholder needs." It is inappropriate to compare this to the explanations of the ANN model. I do not understand why we would want ANN model explanations to agree with LR explanations. Note that this is quite different from what [Agarwal et al. (2022)](https://arxiv.org/abs/2206.11104) did in their experiments. The experiment setup in general is quite confusing.

In line 463, the discussion regards explanation methods as "stakeholders". I question whether it is appropriate to frame it this way as it is difficult to imagine a stakeholder wanting rankings "like LIME".

Furthermore, the paper in its current state does not seem refined. The authors introduce the problem of explanatory multiplicity but do not make an effort to elaborate on how and why it hinders trust in the model (what about the explanation method?). Also, figures 1 and 2 are not helpful in improving the readers' understanding of the EXAGREE process. Figure 1 is especially confusing regarding what it is meant to portray.
Recommendation: 1
Reviewer confidence: 4
Soundness/correctness: 1
Clarity: 2