Venue: NeurIPS 2022 Conference

Reviews:

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: This paper aims to transfer fairness under distribution shift. Specifically, a comprehensive analysis of distribution shift is provided and then defines two types of distribution shift, named subpopulation shift and domain shift. Given the powerful self-training tool to solve the distribution shift problem, the authors first identify a sufficient condition for fairness transfer and then propose an algorithm with fair consistency regularization. Experiments on synthetic
and real datasets demonstrate the effectiveness in terms of fairness and accuracy under various distribution shifts.

Overall, this paper is generally good, in terms of technical novelty and evaluations.

strengths_and_weaknesses: Strengths

1.	This paper is well-written. The authors first provide a fine-grained analysis of fairness under distribution shift and identify the sufficient condition for fairness transfer. Motivated by the analysis of fairness, fair consistency regularization is introduced to transfer fairness under distribution shift. 

2.	The idea of fair consistency regularization is simple yet effective with (partial) theoretical support.

Weakness

1.	There is a gap between theory (Theorem 4.1) and proposed algorithms. Theorem 4.1 implies that minimizing the worst-group consistency loss is beneficial to transfer fairness. However, the proposed regularization employs a balanced consistency loss in practice. It is still unclear, empirically or theoretically, why the worst-group consistency loss cannot work well in practice. Additionally, the dynamic weight seems to be important in the proposed regularization, what’s the rationale to design such dynamic weight?

2.	Marginal gain for the proposed algorithm. In figure 3 and table 1, the accuracy-fairness tradeoff performance seems to be marginal compared with baseline Laftr+FixMatch. Current results only provide one single accuracy and EO performance, it is hard to identify the superiority of the proposed method. A similar issue exists in Ablation study part. It would be better to plot the Pareto frontier of different methods with variable hyperparameters.

questions: Please see Weaknesses.
limitations: Yes
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: In this work, the author analyses the problem of how model fairness is affected under different types of distribution shifts. 
For this, they use equalized odd fairness metrics and propose several types of distribution shifts in the synthetic, real image, and real tabular datasets.
The authors also propose a "theory-minded algorithm" for transferring fairness with a fair consistency regularization. 


strengths_and_weaknesses: The paper deals with a highly relevant topic (fairness and distribution shift) that remains largely unexplored. 

The authors select one measure of fairness equal opportunity odds, that is very relevant and widely used but without any social/redistributive justice justification.

The paper is in general hard to understand. Some of the mathematical formulations are not the traditional ones and they use overly complicated notations that difficult the reading of the paper.

The paper is also not self-contained and over relies on the appendix and external work that is not properly introduced.

Assumption #1 This assumption makes the problem solvable but in real cases the assumption does not hold, as one of the most common and challenging types of shift is concept shift, where the data generation process actually changes.
This happens more often with tabular data, and distinguishing covariate shift from concept shift is not feasible in many situations.
For the folktables data, one could arguably say that predicting US income by training in CA and evaluating has low fairness variations.
If the problem has a higher distribution shift e.g. by changing the prediction task to  "ACSTravelTime: predict whether an individual has a commute to work that is longer than 20 minutes" training in Hawaii and predicting in the rest of the states, assumption #1 will not hold. How would the proposed method work in this case? Would it flag that is not properly working? Will achieve better results than a baseline.



Minor remarks:
 - Line 15 "A synthetic dataset benchmark... is deployed" Is deployed the right verb here?
 - The figures are too small and the font size is considerably smaller than the text

[1] https://arxiv.org/abs/2201.04234
questions: Other authors have clearly limited what relies upon to be possible or not to predict model performance degradation under distribution shift [1]
Do these limitations still apply when evaluating for transferring fairness?

limitations: Other authors have clearly limited what relies upon to be possible or not to predict model performance degradation under distribution shift [1]. This limitation is eluded by stating assumption #1 

The abstract presents a "fine-grained analysis of how the fair model is affected under different types of distribution shift". There is no discussion about concept shift. This should be acknowledged.
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: This paper proposes to use consistency regularization to improve fairness in ML models under distribution shifts. Specifically, the paper extends upon LAFTR (an adversarial learning algorithm for fairness) and FixMatch to impose the consistency constraints via self-training, and for the consistency constraints the authors extend the existing consistency notion with "intra-group expansion", i.e., the transformation/augmentation should be applied within each group and label.

The authors performed experiments on both a synthetic dataset and two real datasets, and show sometimes the fairness in the target domain can be improved compared to existing approaches.
strengths_and_weaknesses: Strengths:
- Fairness under distribution shift is an important research topic, and this paper proposes to use the consistency regularization as a novel way to tackle this problem.
- In general the paper is clearly written, the method and related work are well presented.

Weaknesses:
- The proposed algorithm seems to only work well on synthetic data (Figure 3). When transferred to real datasets, the experiments results are rather weak. 

(1) from UTKFace to FairFace (Table 1), the proposed approach (either with Laftr or CFair) does not achieve the best fairness in terms of equalized odds. For example, Laftr+FixMatch / CFair + FixMathc both achieve good accuracy and lower equalized odds than the proposed approaches. The group accuracy variance does decrease for the proposed approach, but why is that important compared to the fairness metrics? In addition, all the numbers have high standard deviations, are there any significance test done to show the superiority of some of the methods?

(2) on the tabular dataset, the only results shown are in Figure 4. Other than LAFTR, no other baselines are shown, how do we know if the proposed methods work better than other baselines? Even just compared to LAFTR, from the figures (a) and (b) it is very hard to tell if the proposal made the results more fair (as most data points are almost in the same range, and there is no one-to-one correspondence). For Figure 4(c), there is no significance test either, so it is unclear if the results are significant.

- The transformation/augmentation is an important piece in the overall framework, but it is not discussed in detail at all in this paper. The authors only briefly mentioned what transformations are excluded in Section 6.3. As the authors reuse existing augmentations designed for robustness (not necessarily for fairness), they should perform a more detailed study on how each of those transformations affect fairness, and if the effect is positive or negative. 

Minor:
- Figure 3 (a), LAFTER -> LAFTR
questions: - How does the result in Table 1 show the superiority of the proposed methods on target fairness? From the numbers it is unclear if the proposed method works better than Laftr+FixMatch or CFair+FixMatch.

- Why do augmentations designed for robustness definitely improve fairness? Would some of the augmentations (other than obvious ones like color jittering) hurt fairness? The authors should perform a more detailed study on this.
limitations: N/A, this paper aims to improve fairness in ML models.
review_title: null

Meta-review: metareview: The reviews are a bit divergent. While all the reviewers appreciate the clarity of the paper and the theory-inspired proposed algorithm, they raised some concerns e.g., on the assumption employed for obtaining a theoretical result, as well as on marginal (or worse) EO fairness performance in some cases. Although concerns on the fairness performance improvement in light of the employed metrics are still unresolved, many of the concerns are properly addressed, and with regard to the writing quality and insights, I believe that the paper is worth being published. Hence, I recommend the acceptance of this paper.
confidence: Less certain
Decision: Accept