Venue: NeurIPS 2021 Track Datasets_and_Benchmarks Round2

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: summary and contributions: The paper starts by describing the importance of explainable ML to enable debugging, build user trust, and avoid bias. Unfortunately explainable ML requires human evaluators, which make experimentation costly. This paper introduces an automated evaluation methodology    based on synthetic datasets which, though inferior to human evaluation, enables rapid experimentation and identification of best approaches for human evaluators. The authors summarize their CONTRIBUTIONS as (1) synthetic datasets, (2) evaluation of leading explainable ML approaches. The authors provide a good survey of related work in the explainable ML space. They then introduce the explainable ML problem and popular metrics with strengths and weaknesses. Next, they describe their synthetic dataset approach to producing datasets with know feature to label relationships. These known relationships provide ground truth for the output of ML explanatory approaches. The authors apply their synthetic dataset approach to leading explainable ML approaches and report the results. They also use synthetic dataset s to evaluate a single real dataset, WINE. The authors conclude with an explanation of recommend usage, discussion of the societal important of making ML more explainable (e.g. to avoid bias), and their intent to develop and expand this work.
strengths: The paper presents a lasting tool for explainable ML research, an increasingly important branch of ML. Any branch of research that depends on human evaluators faces a considerable hurdle, and this paper can help ease that burden and accelerate progress. The authors general approach is thoughtful and clearly explained, and they support the end-user with advice and consider the societal impact of the work.
weaknesses: The primary weakness of the work is that the authors validate their approach against a single dataset. I would prefer to see evaluation against multiple real datasets to more solidly ensure that the synthetic datasets will point innovation in the right direction. 

Coupling this work with a maintained suite of real world benchmarks, with analysis of their synthetic parameters, would created an invaluable toolbox for explainable ML and I implore the authors to continue in this direction. 
correctness: Yes
clarity: The paper is well organized and well written.
relation to prior work: The paper provides and early and well organized explanation of work in explainable ML. It does not explicitly cover work on explainable ML benchmarking, but that may because no such work exists.
additional feedback: Please continue to invest in maturing this work.
review_title: Good paper on automatic evaluation of ML explanatory algorithms

final_score: rating: 6: Marginally above acceptance threshold
review_content: summary and contributions: In this work, the authors suggest a benchmarking library for 6 black-box ML post-hoc explanation techniques: the codebase is based on synthetic data sets, evaluation measures, and different explanation techniques. Using their codebase, the authors benchmark the xai techniques mostly using already existing evaluation measures.
strengths: - Evaluating the quality of ML explanations is a timely and required task to showcase gaps in current xai literature
- The codebase provides established evaluation measures which will help compare various xai methods in the future
- The 'guide to choosing metrics' is helpful and explains the differences between the metrics well
weaknesses: - The synthetic data sets are rather simple; it is not 100% clear how the weights were generated. Are the weights locally different?
- There is no discussion how the hyperparameters were chosen for the experimental evaluation, while prior work has already suggested that explanations substantially vary depending on hyperparameter settigns. In this light, it is interesting to see that SHAPR performs pretty well across all metrics. In Appendix B, you say that you use default hyperparameters for all methods other than SHAPR - could SHAPR's favourable performance be mainly explained by its hyperparameter optimization?
- It would be great to see other non-post-hoc methods inlcuded; however, your code base is a good starting point as is.
correctness: - The formulae in equations (1) to (4) appear correct. In addition, they are well supported by descriptions of their intuitive meaning.
- Figure 1 does not enumerate 'breakdown' - you might want to add it there
- Why should $g(x,f)=w$ lie in  $[-1,1]$ (ll. 110-111)?
- Your labels are in [0,1]. Does that mean you do regression? Or do you still use cross-entropy loss during training?

clarity: The paper is written clearly.
relation to prior work: Relation to prior work is genereally properly discussed. Additional references should be added to the 'Benchmarking Explainability Techniques'' section; e.g. [1].

[1] CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms, Pawelczyk et al (2021), Openreview
additional feedback: -
review_title: Useful benchmarking codebase to study various xai methods' efficacy, but limited to post-hoc methods

final_score: rating: 7: Good paper, accept
review_content: summary and contributions: The paper presents a useful set of synthetic datasets with known feature weights. The authors' package can be used to benchmark new explanation methods against these datasets, by comparing the feature importances generated by those methods against the known feature weights of the synthetic datasets.
strengths: To my knowledge this is the first such repository of datasets for explainability that aim to be 'benchmarks'. It will be helpful for comparing currently popular explanation methods, and also for empirically evaluating novel explanation methods in the future.
weaknesses: I would have liked to see more discussion of why the different explanation methods tested in Section 5 show significant differences in performance. It would have been informative to see how the performance differences cohere with existing theoretical critiques of the various methods. It would have been particularly interesting to discuss the mechanisms behind how dataset characteristics affect explanation methods in different ways.
correctness: To my knowledge, yes
clarity: Yes
relation to prior work: Yes
additional feedback: Minor comment: The authors cite the distinction between interventional and conditional expectation estimates for SHAP, but it seems that in Section 5.2 they write "Explainers such as SHAP assume feature independence"---if they used interventional expectations in the experiment for this section, this should explicitly stated, otherwise that claim is somewhat confusing.
review_title: Useful datasets/package for benchmarking explanations

Meta-review: Reviewers agreed that this dataset will add values in evaluating explanatory methods. Despite the simplicity of the dataset, reviewers agree that the dataset will help towards unification of benchmarks. Authors responded reviewers questions, for example, by adding a second real dataset.
Decision: Accept