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 proposed a fair contrastive learning method with gradient-based reweighing to learn a fair representation for downstream classification tasks without accessing sensitive attributes. The top-$k$ loss is employed as a surrogate fairness constraint. The convergence of the proposed method is theoretically proved and the effectiveness of the proposed method is verified on benchmark datasets.
strengths_and_weaknesses: Originality: The proposed method is a combination of Min-Max fairness, importance reweighting, contrastive representation learning, and inner-product-based weight approximation. 

Quality: This work is technically sound. The claims are well supported.

Clarity: Overall this paper is well written and organized. However, there are some nations being used without clearly defined. 

Significance: The proposed method is insightful and provides a new perspective on learning fair representations in an unsupervised manner.
questions: 1. The motivation for this work is not strong. The paper does not do a very good job of motivating why sensitive information is infeasible in real-world scenarios. I think the paper would be more compelling if the authors could provide more concrete scenarios where sensitive information is infeasible.

2. The authors claimed that conventional methods on fairness would fail to work when there are more than one sensitive attributes. However, I think the sensitive attributes in previous works are not restricted to being single.

3. In line 119, what are the superscripts c and s?

4. In line 158, what is the subscript $+$? How does $\ell(k,\theta)$ calculate the top-$k$ average loss?

5. It is not clear to me how the proposed method learns fair representations. Can the authors comment on how the weights help to learn fair representations in contrastive learning and how the minimization of classification loss on validation samples helps to learn the proper weights? Is there any physical meaning to the weights? Do they have ground-truth values?

6. Some improvements of the proposed method are marginal. It is better to do a significance test.

7. The size of the validation set seems important. It is better to do an ablation study for it.

======================= After Rebuttal =========================

I increased my score to borderline accept.
limitations: The authors have not adequately addressed the limitations and potential negative social impact of their work.
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: This work introduces a contrastive method that reweighs training samples based on objective loss gradients. The proposed method is very interesting and takes the form of a training procedure (instead of defining more models or editing existing ones), which means that it is particularly suitable for reuse by non-experts. The paper is well-organized, but I think it needs a revision to improve explanations and make in-depth arguments that would help explain why the proposed approach works. The experiment methodology is sound and results are convincing compared to existing alternatives.
strengths_and_weaknesses: - The greatest shortcoming of the paper is that, in section 3.4, the explanation of why the particular weighting strategy was chosen is too brief and thus feels rather arbitrary. In my opinion, a couple of paragraphs are needed to corroborate why this strategy is expected to work. Two particular concerns I would personally like to see addressed (in addition to other explanations of why following gradients can improve fairness) is that gradients may be similar, but integrating over them can create huge deviations in training outcomes in terms of loss and corresponding accuracy (the authors do a nice job in showing that training converges, but is this enough?) and why should a distribution be approximated by relu-ing data instead of normalizing their sigmoid transformation, which is more typical in machine learning.
- The paper measures specific types of fairness, but it is not explained why the proposed approach can address these in particular. Note that addressing multiple types of fairness simultaneously perfectly is known to be an impossible task unless very specific conditions are satisfied [Kleinberg et al. (2016)], which begs the question what kind of fairness objective the proposed approach is particularly good at addressing.

Minor comments:
- In section 2, please give a brief explanation of the main idea of contrastive learning.
- "Here a straightforward way to is to" > incomplete sentence
- "The problem with such method is," > "The problem with such a method is that,"
- It could be helpful to refer to [1] for better positioning the proposed approach in the context of AI bias literature.

[1] Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E., ... & Staab, S. (2020). Bias in data‐driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356.
questions: Please see my comments in Strengths and Weaknesses section.
limitations: Please see my comments in Strengths and Weaknesses section.
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: This paper introduces a novel problem “Can we improve fair classification without sensitive information and without labels?” To tackle this problem, they propose a reweighing-based contrastive learning method. Then, they provide theoretical proof of the convergence of their model. Finally, the conduct experiments on three datasets to show their proposed method achieves better performance. 
strengths_and_weaknesses: Strengths:

1)	The motivation for this paper is intuitive and well explained. Meanwhile, the paper is well organized.
2)	The authors consider a semi-supervised setting where a small validation set is available, which has strong research significance.
3)	The authors propose a novel contrastive learning method and can solve their proposed question well.
4)	The authors provide theoretical proof of the convergence of their method.
5)	The authors validate the effectiveness of our method in improving fairness on three benchmark datasets.

However, I still have some concerns:

1)	The authors consider a semi-supervised setting compared with DRO and ARL. However, there are many semi-supervised methods in classification. Are they can combined with DRO or ARL? why authors consider using contrastive learning methods to improve the semi-supervised setting?
2)	In experiment, the authors compare their methods with DRO and ARL which are under a fully supervised setting. However, their methods get better performance in both accuracy and fairness metrics. I'm a little skeptical about this result. And in experiment analysis, the authors don’t analysis the reasons. What’s the experiment setting of DRO and ARL? Besides, the authors compared their methods with methods with correct demographics. I understand this is not the key point of this paper. But why the authors just compare with Postprocessing and TAC instead of the start-of-the-art fairness improved methods? 

questions: According to the authors, there are weaknesses in this work that have not been addressed. Please refer to the main reviews.
limitations: 1)	Maybe due to the space limitation, the detail of the experiment is not well introduced.
2)	The methods with correct demographics baselines are not the start-of-the-art fairness improved methods.
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: This paper studies the fairness issue in machine learning. It aims to improve fair classification without sensitive information and without labels. Specifically, this paper proposes a novel reweighing-based contrastive learning method to learn a generally fair representation without observing sensitive attributes. The idea of this paper is direct and reasonable, and the experimental results demonstrate the effectiveness of the proposed method.
strengths_and_weaknesses: Strengths:

1.	The paper is clearly written and is easy to follow.

2.	The setting in this paper, without sensitive information and without labels, has high application value and is rarely studied before.

3.	The method in this paper has theoretical guarantee.

4.	Extensive experiments are shown to demonstrate the effectiveness of the proposed framework.

Weakness:

1.	There is a typo in line 160, “… to is to …” —> “… is to …”.

2.	It’s not clear why using top-k average loss. 

3.	More ablation study needs to add. It seems like there are requirements for the size of the validation set when using it as a guide. The authors had better to analyze the effect of different size of the validation set.


questions: 	In the line 132, how to obtain α^' under the situation where sensitive attributes are unknown?

	From the line 160 to 163, why is treating all samples as negative a problem? This sentence is not explained clearly. Could the authors give a more detail explanation or an example?


	The authors describe the λ is a hyperparameter in Appendix, but in the equation 2, it looks like the λ is determined by k and k is a hyperparameter. Is the experiment in A.2 conducted on different k?

limitations: N.A
review_title: null

Meta-review: metareview: The paper proposes a contrastive learning approach to learn fair representations, without having explicit access to sensitive attributes. The approach leverages a small validation set with sensitive attributes to help guide training, by assigning per-sample weights based on the effect on the validation loss. All reviewers were supportive of acceptance, with a common appreciation for the novelty of the problem setting, and the modularity of the proposed approach.

The authors can consider adding citations to works operating in the related setting where there are _noisy_ protected attributes, e.g., Gupta et al., "Proxy Fairness"; Lamy et al., "Noise-tolerant fair classification"; Wang et al., "Robust Optimization for Fairness with Noisy Protected Groups".
confidence: Certain
Decision: Accept