Summary
In this paper, the authors put forward a local, attribution-based explanation method that fits a local, explainable surrogate (a linear model in this case) to perturbation output of the original model. In essence, this is exact scenario as the immensely popular LIME explanation method; however, this work puts forward LINEX which seeks to improve on the robustness of LIME (where robustness is measured as Fidelity, Stability, Invariance, and a new property put forward as Unidirectionality). LINEX proceeds by learning a collection of linear classifiers, each fitting to a different perturbation “environment” and the authors provide an interpretation of their least-squares fitting approach as a multiplayer game. In addition the authors provide some minor theoretical analysis (Theorem 1) which justifies the modeling choices allowing them to give an intuition for why their method works well.
Strengths
The paper provides clear motivation for the need for its method, provides a straight forward development of the method, and provides salient theoretical and experimental arguments for the method.
The experimental evidence in favor of the method is strong, and the method is simple enough that I expect it could be widely adopted into post-hoc explanation tools without too much experimental effort.
The paragraph “Implications of Theorem 1” is nice as it allows the reader to very easily digest an interpretation of why Algorithm 1 achieves the goal of the method.
Weaknesses
The method seems to rely on the diversity of the “environments” in a way that is quite unclear to me. Simply picking gaussians with different covariances seems like it will not give rise to the desired benefits of the method (i.e., it is unclear that simply selecting an ensemble of random environments should be enough to improve over using a single superset random environment). I understand that in general we the method picks the lowest magnitude attribution, which can in turn provide greater stability. We observe, indeed, in table 1 that the method provides little benefit in the cases of FNIST, and CIFAR 10, where perhaps these random environments are not enough. However, in IRIS and MEPS, the random environments have significantly better performance than S-LIME, and I am having trouble fully understanding why this is the case. (See questions section).
While I understand the authors general point about the desirability of unidirectionality, it seems like it is already generally satisfied in LIME, S-LIME, and MeLIME. The only place LINEX has a considerable advantage in unidirectionality is on IRIS (and to some extent CIFAR). Perhaps to underscore this point, the authors could run experiments on something like Adult or any other financial dataset where their motivating example for using recourse is relevant and see if there is a considerable difference. To be clear, I am not suggesting the authors carry out this experiment during the rebuttal phase, but I am simply stating that having a more clear experimental example demonstrating the importance of unidirectionality would be good in future versions of the paper.
There are several works in robustness of explanations (albeit gradient-based and not LIME-based) that are very related to this work but are not included as prior works:
[1] Dombrowski et. al. - https://arxiv.org/abs/2012.10425
[2] Wicker et. al. - https://arxiv.org/abs/2212.08507
These are just two of the more recent works that cover robustness of grad. Explanations. I think the more seminal work might be a single, sufficient citation to provide readers with more context:
[3] Dombrowski et. al. - https://proceedings.neurips.cc/paper/2019/file/bb836c01cdc9120a9c984c525e4b1a4a-Paper.pdf
Questions
Can the authors further elaborate precisely what they mean by “perform bootstrap sampling to create k different environments.” I would like to understand exactly how is this done step-by-step such that you get such an incredibly meaningful difference between the output of your method and that of S-LIME.
Why is there a trailing 1 in Equation (2)?
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.
Confidence
3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Limitations
See weaknesses.