Summary
This work is embedded in the research on model-agnostic explanations, i.e., to provide the user an understanding on the outputs of otherwise black-box prediction methods without knowing about the model's internals. While LIME is a popular approach to solve this problem, prior work has demonstrated LIME to suffer from a strong dependence on the random seed, leading to instability and inconsistency. Since reliable, model-agnostic explanations will be a crucial tool for research and application alike to afford the use of otherwise black-box machine learning models, this paper is tackling an important issue considering LIME's popularity yet evident short-comings. GLIME is presented as a step towards more general, stable and consistent model-explanations. Due to the free choice of its sampling distribution and weights, it is shown how GLIME not only improves on LIME but generalizes over other methods.
Strengths
1.The method GLIME is presented very clearly. Not only are text and equations supporting the reader in understanding the method well, but its motivation as successor of LIME in terms of stability and local fidelity are easy to follow and well justified by both the presented related work as well as this paper's own evaluation.
2. The unification of various model-explanation methods not only gives the reader an overview of how these methods relate to each other but shows well how GLIME is not only succeeding at stability and local fidelity but also a more general framework then LIME.
Weaknesses
1. This work is strongly focused on comparing GLIME and its variants to LIME. While the relation of LIME and GLIME are made clear and well supported by the experiments, a more comprehensive overview on the field of explanation methods other than LIME could help the reader to better understand how GLIME fits into current research. Similarly, a discussion of GLIME's short-comings and an outline of Future Work would reinforce the contribution. Along the same line, a discussion on GLIME's quality as model-explainer and human-interpretability of the achieved results would greatly support the claims.
2. While the figures present the concepts and results of this paper quite well, they could benefit from some additional attention and polishing. For example, Fig 1a misses an explanation of the employed colormap. Fig. 1b shows GLIME-Gauss as blue dot in the legend but not the graphic itself. In Fig. 4a, the legend occupies important parts of the plot such that GLIME-Binomial and GLIME-Gauss curves are hard to see.
3. The use of inline-math can at times be overwhelming, e.g., in Theorem 4.2. While it is important to state all the relevant equations and relations, reverting to display- rather than inline-math for the key concepts might help the reader to better digest the underlying theory and assumptions.
Questions
1. What are GLIME's short-comings and what are plans to improve on the method in the future?
2. Has it been evaluated if there is a difference in how human-interpretable GLIME's and LIME's explanations are?
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
4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Limitations
Overall I would say it's a technically sound paper, but for a model-explanation paper I am missing the human-side a bit. My understanding is that the improvements the work shows are only meaningful if the consistent/stable explanations are also still good explanations, which I think is not really discussed here.