Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: 
This paper provides an extensive empirical analysis of the subgroup robustness of tree-based models (like XGBoost), distributionally robust models (like DRO), and fairness-promoting methods (like fairlearn) on five commonly used datasets for fairness-aware machine learning: ACS Income, Adult, Communities and Crime, COMPAS, and German Credit. For each model type, they train many models over a grid of hyperparameter values, and they report the worst-group accuracy, accuracy, max disparity between groups, and cVaR (a measure of accuracy in the tail). They find evidence that tree-based models show good subgroup-robustness properties, often on par with distributionally robust methods and with fairness-aware methods. 
strengths_and_weaknesses: # Originality

This empirical investigation is original and needed. Prior work (discussed in the paper) had conducted an empirical comparison of fairness methods early in the literature, but the modern update to include distributionally robust methods is original. 

However, some of the claims of contributions in the paper are not well supported. The paper claims to “introduce techniques for the analysis of hyperparameter sweets such as model performance frontier curves” (line 62-63), but it is not clear how this is different from commonly reported Pareto frontier curves. 

# Quality

Given recent works offering more extensive and more realistic datasets, I am not convinced that it is sufficient to perform this empirical analysis on the five traditional fairness datasets. A comprehensive empirical analysis of the Adult dataset should include analysis on other “folktables” as provided in Ding et al. Additionally, Koh et al. curated a list of datasets that contain “in-the-wild” distribution shifts including subpopulation shift. This seems like an important set of datasets to perform analysis on.

No uncertainty estimates are provided. In the checklist the authors claim they are “not concerned with point estimates” but most of the metrics reported here are point estimates, right? Relatedly, it may be helpful to present results across datasets—is there a way to graphically summarize the range of results you see across datasets? This might be helpful for readers who want to get a quick overall summary of the results. 

Ding, Frances, et al. "Retiring adult: New datasets for fair machine learning." Advances in Neural Information Processing Systems 34 (2021): 6478-6490.

Koh, Pang Wei, et al. "Wilds: A benchmark of in-the-wild distribution shifts." International Conference on Machine Learning. PMLR, 2021.

# Clarity

The paper has a strong introduction with a nice discussion of the relationship between fairness and robustness.

However I generally found the remainder of the paper hard to read. For the exposition of the methods, it would be helpful to provide inline text describing the fairness and robust methods you include. It would also be useful to provide more discussion/intuition about why trees seem to have this robustness. It may be relevant to discuss prior work that analyzes when fairness-aware methods can address subpopulation shift: 

Maity, Subha, et al. "Does enforcing fairness mitigate biases caused by subpopulation shift?." Advances in Neural Information Processing Systems 34 (2021): 25773-25784.

Singh, Harvineet, et al. "Fairness violations and mitigation under covariate shift." Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 2021.

 I found the designation of “complementary” metrics (line 245) to be confusing, and it would be helpful to add more details here to explicitly define what makes a metric complementary to another (and perhaps elaborate on an example).

I found it hard to digest many metrics reported over many graphs, and at times hard to make sense of the reported trends. For instance in Figure 1a the XGboost curve seems to track the y=x line, so does this indicate XGBoost had the same accuracy across all groups? Additionally, it was unclear to me what the difference between graph 1a and 1b was. It would be helpful to elaborate in the captions, including describing what the baselines refer to (or at least a pointer to the description in the text).


# Significance

This paper has the potential to be quite significant. Subgroup robustness is desired in many applications and their contribution showing that trees can have subgroup robustness is notable. The significance would be even stronger if the paper offered concrete takeaways/recommendations: e.g., in practice how should one make use of the findings here? For this it may be helpful to discuss the hyperparameter grid sweep in the main paper. 

questions: - Can you provide any discussion on why grid search over tree hyper parameters seems to trace out the fairness-accuracy Pareto front?
- Similarly, can you provide motivation for why trees might have subgroup robustness?
- Is it possible to update the main text to include some details on the hyperparameter sweeps and other important experimental details?
limitations: There is no proper discussion of the limitations of this analysis. I would recommend that the authors add this. For instance, what are the limitations in extrapolating these findings to other datasets, other domains?
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: Through extensive experiments, the authors demonstrate that tree-based methods are a strong and computationally frugal baseline for tabular prediction, ultimately achieving favorable performance in terms of overall and subgroup performance.

strengths_and_weaknesses: Strengths
* The authors investigate subgroup robustness properties of tree-based models on tabular prediction, which I haven’t seen before and believe will be interesting to the neurips community (although tree-based methods are known to be a strong baseline for tabular benchmarks in general).
* Quality of presentation is high. Baseline methods and empirical results are presented in an accessible way.
* Very nice related works section that would serve as a useful mini-survey for the interested reader.

Weaknesses
* What seems to be missing is some reasonable hypothesis for *why* tree-based methods tend to find subgroup-robust models. Cynthia Rudin has an extensive body of work arguing that tree-based methods (e.g. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122117/)  are very effective modeling tabular data, and may be better equipped to find a good solution than deep learning based approaches. The results presented here are consistent with that hypothesis since we see that the tree-based methods are best at robust metrics precisely because they are best at overall accuracy, and the authors show that aggregate accuracy and subgroup accuracy are very correlated on this data. It makes me think that the main takeaway could be that trees are effective tabular learners, rather than subgroup-robust learners (although these are not mutually exclusive).
questions: Race and gender and gender are listed as examples of binary sensitive attributes [line 140]. While these variables may be codified as binary in the dataset, they are not binary in reality [https://arxiv.org/abs/2205.04610, https://dl.acm.org/doi/10.1145/3411764.3445742]. Making this distinction is important, especially in settings where the data collection is beyond our control (e.g. fixed benchmark datasets).
Typo in line 273: “evaluated basedon”

limitations: I think the authors could do more to contextualize the specific tabular prediction tasks evaluated. For example the use of tabular methods for risk assessment and neighborhood crime-level prediction could have negative societal impacts, even if they achieve some notion of statistical fairness or subgroup robustness. Cf. https://arxiv.org/abs/2106.05498
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The paper empirically evaluates subgroup robustness methodologies on tabular data with clearly defined subgroups. They posit that state of the art tree-based methods are already robust learners in the subgroup-robust sense, and that they outperform the existing state of the art fairness and robustness methods in these categories. Further, they show that existing subgroup robustness methods can be brittle (i.e., show poor performance under other robustness criteria) 
strengths_and_weaknesses: Strengths:

The paper is clear in its goals and methodology, and the experiments show that worst group loss and worst disparity between subgroups are very well addressed by existing tree-based methods on tabular data, even outperforming many existing subgroup fairness and robustness approaches.

Weaknesses:

The vast majority of subgroup robustness and fairness algorithms are evaluated on MLP-based architectures. Though this is a limitation of current fairness and robustness work, it is hard to draw conclusions on the field at large other than the need for a greater focus on methods that work on tree-based architectures, or on better base architectures for tabular data specifically.

It is not clear to me whether these results are surprising, since a sufficiently large improvement in overall model accuracy is warranted to also improve worst-group accuracy.
questions: What do the authors see as the main limitation for the standard robustness- and fairness-based methods to work on tree-based architectures?

Do the authors believe that tree-based methods cannot be further improved by the use of robust learning?
limitations: No dedicated section was provided, the work itself highlights potential areas for improvement in the area, which by itself is a net positive impact.
review_title: null

Meta-review: metareview: This work looks at sensitive subgroups' propensity to be incorrectly scored across both "traditional" tree-based models and other approaches from the recent literature, on tabular data.  I believe Reviewer kdGJ's numeric score is lower than it should be (thank you, Reviewer kdGJ for a technically strong review and for participating in a back-and-forth with the authors), but I also agree with the concerns that were surfaced here.  I also agree with the questioning of Reviewer WCHM w.r.t. domain generalization, and believe that this is more central to the story of the paper than is presently written.  A borderline paper, I would encourage the authors to seriously consider both of those reviewers' threads in a camera-ready or next submission.
confidence: Less certain
Decision: Accept