Venue: NeurIPS 2022 Conference

Reviews:

final_score: 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.
review_content: summary: This paper proposes a post-processing method to generate fair multi-class classification models based on the information projection technique which formulates fairness as a linear projection-based constraint. Theoretical guarantees on the uniqueness and approximation quality of the optimal solution are derived, and experimental results on real-world data sets demonstrate effectiveness of the proposed method in both binary and multi-class classification tasks. 


strengths_and_weaknesses: Strength 
- Fair multi-class classification is a timely and useful topic. 
- The proposed method looks novel and has theoretical guarantees. 
- The paper is well-written in general. 

Weakness 
- It is not explained how "multi-class fairness" is defined and how the formulated linear projection-based constraint achieves that. 
- A large portion of the experimental results focus on binary classification instead of multi-class classification. 


Detailed Comments

Overall it looks like an acceptable paper. The idea of using information projection to model fairness looks novel and interesting, and theoretical guarantees are derived. 

My main complaint is on the missing explanations of two things: (1) the definition of multi-class fairness, (2) how the formulated linear projection-based constraint achieved that (it is not explained in the supplementary material either). Please clarify them. 

Experimental results look a bit weak, because a large part of them focuses on the binary case and only Figure 2 is for multi-class classification. (Same pattern in the supplementary material.) I suggest authors expand experimental results on the multi-class case to support the main contribution of this paper. 

Also, there are two existing fair multi-class classifiers in Table 1. Why only one of them is being compared in experiment? 

Finally, it might be helpful to discuss the fairness of h_base. My understanding is Theorem 4 suggests the trained model can well approximate the most accurate model h_base (probably to the optimum degree). If h_base is far from fairness, would the trained model still be fair? Or, would there be any conflict between this approximation and the formulated constraint?   
questions: 1. Please clarify the definition of multi-class fairness. 

2. Please clarify how the formulated linear projection-based constraint achieves that definition. 

3. Please try to enrich the experimental results for the multi-class case. 

I will raise my score if the above questions are adequately addressed. 
limitations: Yes.
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper introduces a procedure for improving the fairness of models in the multi-class classification setting using a technique called information projection. In practice, this technique involves reweighting the outputs of a pre-trained model. The authors provide a theoretical basis for the technique and empirically evaluate on multiple multi-class benchmark datasets. Additionally, they introduce a new, larger scale dataset of Brazilian high school assessments (1M examples) that is to be released with the paper.
strengths_and_weaknesses: Originality: The techniques introduced are an application of the information projection technique for the purposes of improving fairness of multi-class prediction systems. This work is original in multiple senses. First, it goes beyond much of the literature just by considering the multi-class setting as opposed to the typical binary setting. It introduces a new reweighting technique that is competitive with other existing techniques while generally scaling better for large datasets. In addition to the novelty of the technique itself, I found that the focus on a more complex setting and the addition of practical considerations like the runtime of the technique distinguish this work from others. Additionally, it introduces and releases a completely new benchmark dataset for the community. 

Quality: While I did not verify all of the proofs in the work, the presentation is high quality and claims are rigorously justified throughout. 

Clarity: In general, the presentation was very clear. It was easy to get lost in the math in the middle of the paper, and I suggest that the authors move some of this material into the supplemental material. In particular, some of the introductory material on pages 3 and 4 on information projection could be moved, as well as the details of the theoretical guarantees of FairProjection on page 7. This will make the paper more easily digestible, and they are not necessary for understanding the empirical results shown later. 

Significance: The results shown demonstrate improvement over existing techniques, both in the scope of their applicability (some of those compared to are only applicable in the binary case) and runtime constraints. With the release of code and data, it does seem that this work would be very usable by practitioners who work in the multi-class classification setting. Additionally, the newly released dataset should allow many new papers to benchmark against a tougher problem than currently exists in the literature. 

questions: The questions I list here are ones that I think, if the authors were to clarify in the text, would strengthen the paper overall. 
 
What kinds of fairness constraints are not expressible in the form shown on line 119? That is to say, is the work limited to the criteria in table 2, and if not, what are the limits?

In figure 1, there are multiple points for each technique? It seems from line 280-281 that this is because of varying alpha. If so, please clarify in the caption of the figure. Additionally, it would be nice to indicate which direction changing alpha corresponds to moving on the curves.

limitations: The key limitation is the expressibility of the fairness constraint as an inequality. It would be good to get more clarity on which fairness metrics satisfy this. Additionally, the technique is limited to datasets where group membership is known. It would be interesting to know from the authors if there are any worst-case guarantees that can be made if the groups are only partially known, but this is more for interest rather than a necessary component of the paper.
review_title: null

final_score: 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.
review_content: summary: The authors tackle the problem of achieving probabilistic group fairness through post-processing. They propose a method based on information projections -- projecting an unfair classifier into the set of fair classifiers, which corresponds to a simple tilting. They propose an algorithm to solve this optimization problem, and derive sample complexity and convergence guarantees. The authors show that their method achieves competitive performance-fairness tradeoffs relative to baseline methods, while having a faster runtime, on both binary and multiclass tasks.
strengths_and_weaknesses: Strengths:
The idea of achieving fair classification through information projection is novel to the best of my knowledge. The proposed method is intuitive, and the authors prove many important theoretical properties. It also achieves decent empirical results with low runtime. The paper is rigorous and well-written. 

Weaknesses:
1. The method proposed is targeted towards achieving the probabilistic definitions of group fairness, whereas most other work in the algorithmic fairness literature deal with the thresholded (binary) case. The authors should briefly comment on how the two are related -- are there cases where one implies the other?
2. The authors should comment briefly about how they selected their baselines. Why were some methods from Table 1 not used in the experiments, and why were [CJG+19] and others omitted from Table 1?
3. Theorems 2 and 3 from the paper seem to be very similar to Theorem 3 from [AAW+20]. The authors should clarify how they differ.
4. Any performance improvements of the proposed method over [ABD+18] is quite marginal (Fig 1), though there is a significant runtime improvement. How do the convergence or sample complexity guarantees of this method compare with FairProjection? Further, does the convergence rate of FairProjection depend on the number of groups?
5. In Table 3, I would recommend adding in standard deviations, as well as showing these results for the other datasets as well.
6. The multiclass results (Figure 2) are not particularly convincing as there is only one dataset and one baseline method. Are there any simple baselines or additional datasets that can be included?
7. The authors should reference [1] in their related work, and potentially benchmark against this method.
8. I would suggest being more specific in the title about the proposed method.

[1] Kim, Michael P., Amirata Ghorbani, and James Zou. "Multiaccuracy: Black-box post-processing for fairness in classification." Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. 2019.
questions: Please address points 1-6 from above.
limitations: The authors adequately address most of the limitations in Section 6. I would recommend adding a sentence about how group fairness may not be desirable in the real world because it often leads to reduced utility for all groups [1, 2]. 

[1] Corbett-Davies, Sam, and Sharad Goel. "The measure and mismeasure of fairness: A critical review of fair machine learning." arXiv preprint arXiv:1808.00023 (2018).

[2] Hu, Lily, and Yiling Chen. "Fair classification and social welfare." Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 2020.
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: The authors of this paper present an approach to tabular fairness problems beyond sinlge-class classification problems. The other main contribution of this work is the use of a new tabular dataset with over one million samples on Brazil’s national high school exam. The technical approach the authors take is within the post-processing line of research in the fairness domain where they innovate with an approach to account for multiple protected groups and which is scalable to larger datasets. 

strengths_and_weaknesses: Strengths:
1. The innovation in the approach and solution for this paper are very high. I believe that this work will have moderate impact in the field and will spur further research on both the dataset and the parallelizable, multi-class approach. 
2. The use of the new dataset is much appreciated both for its size and need in this community which for too long has relied on Adult and COMPAS to prove new methods. The experimentation section adequately benchmarks available algorithms against these data as well.
3. The paper includes an extensive theoretical section and convincing experimental results as well. 

Weaknesses:
1. The authors could compare to more approaches which are not post-processing style fairness algorithms. However, I don’t believe this is necessary for this paper’s acceptance as I find it to be out of scope of the acceptability of this paper. 

questions: Question: How would the authors believe their work would accommodate notions of intersectionality? Could the authors speak to the increasing work and critical analysis of algorithmic fairness through the lens of intersectionality (https://facctconference.org/static/pdfs_2022/facct22-28.pdf, https://facctconference.org/static/pdfs_2022/facct22-49.pdf, https://facctconference.org/static/pdfs_2022/facct22-122.pdf)

Suggestion: I strongly suggest the authors tone down the language of “introducing” the dataset. According to the appendix, the authors were not involved in the data collection process and they merely link to the data. Suggesting that these authors “introduced” these data is irresponsible. A more honest phrasing would be that the authors “use” the existing dataset.

limitations: The authors have adequately addressed the limitations and potential negative societal impact of their work.
review_title: null

Meta-review: metareview: I recommend acceptance for this paper due to the uniformly positive reviews, which emphasize the relevance and novelty of the proposed method for fair multiclass classification. The method is well-supported experimentally, and the authors introduce to the ML literature a new benchmark classification task. During the discussion period, many of the reviewers concerns around baselines, related work, etc were addressed.
confidence: Certain
Decision: Accept