reviews: 
Comments: The paper begins with an implicit premise that there exists an ideal model explanation for a given set of model predictions. Given this kind of explanation obtained from an oracle, the paper presents an algorithm to select a model from a Rashomon set of models that matches the expected explanations. The paper terms this as an SAEM (stakeholder aligned explanation model). "Explanation model" here does not seem to refer to an explanation method, but a predictive model (one picked from the existing Rashomon set) that best matches the explanations desired by a stakeholder.
Strengths: The scientific contribution presented in the paper is valid and interesting. The proposed method has the means to create machine learning models that can produce explanations that match an arbitrary ideal. The paper presents a new, model-agnostic method to accomplish this, which would be a meaningful and important contribution to the field.
Weaknesses: The paper’s formalization of four types of disagreements lacks sufficient motivation and clarity, especially in comparison to the foundational work it draws from. For example, the classification of "model disagreement" as a type of "explanation disagreement" is unclear - different models that produce the same predictions can indeed have different internal mechanisms of doing so - in this case explanations should disagree and illuminate this fact rather than obscure it. This notion aligns with the concept of Rashomon sets, where multiple models with similar predictive performance can have significantly different decision boundaries. I encourage the authors to clarify their rationale for categorizing model disagreement within the explanation disagreement framework and to elaborate on how their approach handles cases where differing explanations for similar predictions might reveal essential model behaviors rather than obscure them. There is no "model disagreement" problem.

The paper's contributions then are better studied and understood in the context of a related line of inquiry about adversarial attacks and explanation fairwashing, such as Slack et al (https://doi.org/10.1145/3375627.3375830). The primary problem addressed appears to involve modifying the explanations produced by a Rashomon set of models to align with a predefined set of explanations from an external oracle. This connection is effectively illustrated, though not explicitly addressed, in Table 1, where metrics such as FA, RA, SRA, and others are reported for the ANN model. Notably, the original OpenXAI benchmark (Agarwal et al.: https://dl.acm.org/doi/10.5555/3600270.3601418) does not provide ground truth for ANNs. What this paper does (I think) is use the LR coefficients as ground-truths to measure metrics such as FA, RA, SRA, etc against a **different** model - an ANN! I recommend the authors clarify their novel approach to calculating these metrics for the ANN model and discuss the ethical implications of aligning ANN explanations with LR model coefficients.

Finally, the paper’s entire framing around "explanation agreement" (motivated in Section 2) could be made clearer. Rather than resolving "model disagreement," the proposed SAEM approach seems to modify model explanations without altering predictive accuracy, which could be viewed as a form of adversarial attack on explanations. I encourage the authors to address how this approach contrasts with adversarial manipulations of explanations (if at all), discuss potential connections to explanation fairwashing, and consider any ethical implications that arise from intentionally adjusting explanations while maintaining predictive outputs. What is presented in this paper as an SAEM to resolve the apparent "model disagreement" class of explanation disagreement problems is essentially a means to make the FIS score for the ANN model to match the coefficients from the LR model trained on the same data - this is an adversarial attack.
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 1