From: NeurIPS.cc/2022/Workshop/TSRML/Program_Chairs
title: Paper Decision
decision: Accept

From: NeurIPS.cc/2022/Workshop/TSRML/Paper42/Reviewer_JRv7
title: Good paper with theoretical and empirical results
summary: This paper mainly focuses on providing a theoretical analysis to study the connection between adversarial robustness and algorithmic recourse. They show that improving adversarial robustness increases the cost and reduces the validity of recourses constructed by different existing methods. They also show that the empirical cost differences are within the derived lower and upper bounds, and conduct experiments to demonstrate that the validity decreases with the increase of $\epsilon$ value.
strengths: To my knowledge, there is no prior work investigating how adversarially robust models affect the recourse outputs, and the paper presents interesting theoretical and empirical analysis.
weaknesses: By definition of algorithmic recourse, it is not surprising that adversarially robust models incur more costs for producing recourses.
overall_recommendation: The paper is relevant to the workshop, and presents good theoretical and empirical analysis for a new topic. Therefore, I recommend an accept.
overall_rating: 7: Good paper, accept
review_confidence: 3: The reviewer is fairly confident that the evaluation is correct

From: NeurIPS.cc/2022/Workshop/TSRML/Paper42/Reviewer_sjZs
title: Review
summary: The paper studies the difference of recourse cost and validity between robust and non-robust models. 
It first theoretically analyses and gives bounds for the difference between recourse cost and then theoretically proves that when applying the same recourse algorithm on robust and non-robust models, result recourse for robust models will have lower validity.
strengths: * This paper did a thorough theoretical analysis on the difference of cost and validity for recourse of robust models.
* Writing is neat and clear
* The theoretical results provide a way to measure and compare the trade-off between adversarial robustness and recourse cost.
weaknesses: * In Theorem 1, the result is not specifically analysed for robust and non-robust models but for generally two different models. Therefore this cannot reveal the connection between adversarial robustness and cost for recourse.
* The paper can be much strengthened if it can analyse the trade-off between adversarial accuracy and cost/validity for algorithmic recourse.
overall_recommendation: I recommend the paper to be accepted since it studied the trade-off of adversarial robustness in an important application area.
overall_rating: 7: Good paper, accept
review_confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct