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: The authors argue that training on distilled versions of existing classifiers improves fairness on unobserved demographics and build upon this objective. The approach is interesting and of use to the fairness literature, improving upon previous works. The paper is well-written, the experiment methodology is sound, and results convincing. 
strengths_and_weaknesses: I have a few important concerns, though these are -in my opinion- straightforward to address:

- Given that the authors already arrived at Equation 9, why not use it directly for reweighting and then new training of the base model? I guess the only difference is whether new sample weights or new equivalent objectives are introduced, in which case I would like to see some discussion of this point in the text.
- The question of why disparate impact is improved is not really addressed. The improved theorization editing I mention in the following point could help address this, the authors can give a simple intuitive explanation / proposition, or ideally (though this could be too much work) a second theorem could be introduced. I remind that fairness is not a global objective and that efficacy over each sub-objective needs to be explored by its own. If none of the aforementioned actions are taken, I would also be happy seeing the paper emphasize more on improving equalized odds as the primary goal and mentioning that disparate impact is also improved as a side-effect (at least one hypothesis of why this holds true is needed - maybe something that starts with the assumption that parts of these biases could be correlated so partially fixing one also partially fixes the other and then working from there?).
- The authors argue in section 3.4 that the effectiveness of their approach can be attributed to down-weighting misclassified samples, which has been shown [Krasanakis et al., 2018] to be inherently insufficient by itself. As a recommendation on how to improve this (other explanations could be possible, perhaps even more intuitive depending on phrasing) with small adjustments to the text is to acknowledge that Equation 9 effectively introduces a different degree of weight skewing for highly misclassified samples (i.e. weights are not linear with respect to errors, since t(x) is also implicitly affected by errors) . So, protected demographics (i.e. the groups of people classifiers often bias against) are expected to obtain the highest misclassification rates and this is why placing disproportionately more weights on higher misclassifications addresses bias without impacting accuracy much.

Just a note:
It seems to me that Equation (9) is an ad-hoc variation of [Krasanakis et al., 2018]'s Equation 10 with some additional assumptions filling in for the absence of demographics. More analysis is needed to show equivalence for sure, and it is not needed by the paper's theorization, so I'm just mentioning this in case the authors find it interesting. If this holds true, there is a chance that tweaking the softmax's hyperparameter instead of selecting it with cross-validation could also create accuracy and fairness trade-offs, as already demonstrated for the hyperparmeter `a`, since the former implicitly affects the Lipshitz constant of sample weights.

Minor recommendations:
- in the abstract "Max-Min fairness objective." > "Max-Min fairness objectives."
- in the abstract "to achieve expected improvement in" > "to improve"
- I think the introduction needs a sentence to explain *why* the proposed approach is expected to trade-off fairness with accuracy.
- It would help adding [1] as a general background reference and [2] to labelling bias references in 3.2.
- If I understand this correctly, in section 3.2, probably explicitly mention that Binary corresponds to the original version of the Adult dataset.

[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.
[2] Iosifidis, Vasileios, and Eirini Ntoutsi. "Adafair: Cumulative fairness adaptive boosting." Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019.
questions: Please see my comments on Strengths and Weaknesses section.
limitations: - Differences in Table 1 do not seem statistically significant in terms of disparate impact with a first glance due to high variance, so maybe a little more discussion is needed to address this (they are statistically significant for equalized odds though).
- In my opinion, the fact that the hyperparameter `a` can only be tweaked means that it is currently selected blindly. I think the authors need to be a little more explicit over this shortcoming. Maybe future works can try selecting that hyperparameter with few-shot tweaking (have sensitive attribute for a few samples and then reach an acceptable trade-off)?
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 introduces a new method to achieve considerable improvement in group fairness through knowledge distillation. The solution is to use the soft labels from an overfitted teacher model to train a student model, and the paper analyzes the fairness of the solution theoretically. The experiment results on several extensively-used datasets indicate that the proposed method enhances the fairness in the outcome without loss of Accuracy. The main contribution of the paper is that the authors propose an alternative method when demographic information is not available in the training set. 
strengths_and_weaknesses: Strengths:
1. The paper is clearly organized and well written. 
2. The authors propose an alternative method when demographic information is not available in the training set. The paper analysis the impact of label smoothing and knowledge distillation on fairness theoretically.
3. The experiments demonstrate the effectiveness of the proposed framework, i.e. the improvement of fairness without loss of Accuracy performance.
4. The research direction is interesting and the proposed method is relevant to many domains.


Weaknesses：
1. At the beginning of the paper, it needs a better description of the motivation and novelty; it's hard to know where the challenge is to solve the fairness problem through knowledge distillation. 
2. Besides, except for the theoretical proof of the knowledge distillation on the fairness problem, the paper needs to introduce the reason why it is useful empirically or intuitively to help readers understand better. 
3. The interpretability of the proposed method should be discussed. For example, when demographic information is not available, how to discriminate different groups, such as gender.
4. The procedure of the proposed method by authors on the fairness is not shown logically and intuitively. Adding a figure and refining relevant paragraphs to describe the procedure of how the knowledge distillation can improve fairness may help readers who is not familiar with fairness understand better.
5. There are some typos in this paper. For example, the “individual” shall be “individual” in line 148, and the formula in line 227 may be wrong.

questions: As for the questions and suggestions, please refer to the Weakness in the “Strengths And Weaknesses”. Nevertheless, I still have some concerns:
1. Are the teacher model and student model both trained without the demographic data? Why the teacher model is expected to overfit training data (in line 250)?
2. Did the paper miss some important baselines or references? For example, 
(1)	Celis, L. E., Huang, L., Keswani, V., & Vishnoi, N. K. (2021, July). Fair classification with noisy protected attributes: A framework with provable guarantees. In International Conference on Machine Learning (pp. 1349-1361). PMLR.
(2)	Celis, L. E., Mehrotra, A., & Vishnoi, N. (2021). Fair classification with adversarial perturbations. Advances in Neural Information Processing Systems, 34, 8158-8171.
(3)	Tianxiang Zhao, Enyan Dai, Kai Shu, and Suhang Wang. 2022. Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (WSDM '22). Association for Computing Machinery, New York, NY, USA, 1433–1442.
Other details are as above.

limitations: Please refer to the main reviews.
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 proposes to use knowledge distillation to improve fairness in the student model. The key idea is to use soft labels from the teacher, which essentially poses a smoothness constraint in the student model, to improve fairness (disparate impact and equalized odds).

The authors did experiments on 3 datasets, new Adult, COMPAS, and CelebA, and showed that the proposed method improves upon existing baselines, including a DRO based approach and an adversarial re-weighting based approach.
strengths_and_weaknesses: Strengths:
- This paper is well written. The key idea is clearly presented. The authors first show that label smoothing can help fairness, and then proposed a reasonable approach to practically implement this through knowledge distillation when continuous labels are not directly available.
- The idea of imposing smoothness in the model to improve fairness through knowledge distillation is novel to the best of my knowledge.
- The authors did experiments on 3 datasets, and the results show that the proposed method works universally well across all datasets, outperforming existing DRO and adversarial-reweighting baselines.

Weaknesses:
- Since the authors study label smoothing, one obvious baseline seems to be missing, which is training the model by smoothing the binary labels directly from [1], i.e., using $y(1-a) + a/K$ where $a$ controls the smoothness and $K$ is the number of classes. 

[1] When Does Label Smoothing Help? Muller et al. NeurIPS 2019.

- By looking at the results, there might actually be multiple components that contribute to the final fairness which are entangled together. (1) label smoothing does help the final fairness, as mentioned in Section 3.2. (2) model capacity also seems to contribute to the fairness, as the student trained with hard label already has much better fairness across multiple datasets. 

For completeness, I think the authors should disentangle those factors, i.e., separating the tables into two parts, 
- first part is high-capacity model only (i.e., the teacher), and there should be baselines like a) Teacher (with hard label), b) Teacher with direct label smoothing, c) Teacher with DRO/ARL; 
- then the second part should be low-capacity model only (i.e., the student), and you should list all the results currently presented. 

This should give people a clearer picture about the contribution of each component.
questions: - Can the authors include a baseline that does direct label smoothing on hard labels in training?

- Can the authors add capacity-separated baselines, like Teacher + DRO / ARL?

- Would a combination of Teacher + DRO / ARL + label smoothing further improve fairness? i.e., you distill a model from Teacher + DRO/ARL through the label smoothing you proposed.

- Minor: in Lahoti et al., 2020, the AUC doesn't seem to decrease significantly across Adult (the older dataset) and COMPAS, why it shows a significant drop on the student model compared to student (with hard label) across Table 2/3/4/5?
limitations: Discussed in Section 5.
review_title: null

Meta-review: metareview: Overall the reviews are positive, leaning towards accept. The reviewers agree that the main idea is interesting and the paper is well written and structured. Also several issues raised are properly addressed and checked, and I think that the remaining issues are also taken care of. Hence, I recommend the acceptance of this paper. 
confidence: Certain
Decision: Accept