FairDD: Fair Dataset Distillation via Adversarial Matching

Authors: Qihang Zhou, FangShenHao, Shibo He, Wenchao Meng, Jiming Chen
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Fair Dataset Distillation, Fair Dataset Condensation

Abstract:
Condensing large datasets into smaller synthetic counterparts has demonstrated its promise for image classification. However, previous research has overlooked a crucial concern in image recognition: ensuring that models trained on condensed datasets are unbiased towards protected attributes (PA), such as gender and race. Our investigation reveals that dataset distillation (DD) fails to alleviate the unfairness towards minority groups within original datasets. Moreover, this bias typically worsens in the condensed datasets due to their smaller size. To bridge the research gap, we propose a novel fair dataset distillation (FDD) framework, namely FairDD, which can be seamlessly applied to diverse matching-based DD approaches, requiring no modifications to their original architectures. The key innovation of FairDD lies in synchronously matching synthetic datasets to PA-wise groups of original datasets simultaneously, rather than indiscriminate alignment to the whole distributions in vanilla DDs, dominated by majority groups. This synchronized matching allows synthetic datasets to avoid collapsing into majority groups and bootstrap their balanced generation to all PA groups. Consequently, FairDD could effectively regularize vanilla DDs to favor biased generation toward minority groups while maintaining the accuracy of target attributes. Theoretical analyses and extensive experimental evaluations demonstrate that FairDD significantly improves fairness compared to vanilla DD methods, without sacrificing classification accuracy. Its consistent superiority across diverse DDs, spanning Distribution and Gradient Matching, establishes it as a versatile FDD approach.

Decision: reject
Reviewer scores: 3, 6, 8, 6

Reviews and discussion:
<thread>
creation date:
1730583941958

thread_type:
official_review

<thread_object>
creator: reviewer_sjg8

<content>
summary:
This paper addresses attribute imbalance in dataset distillation, focusing on improving the fairness of condensed datasets. The authors present a unified perspective on data matching and identify unfairness issues in conventional distillation methods regarding protected attributes. To address this, they introduce an adversarial matching loss function that ensures equal contribution from different attribute groups. Their theoretical analysis demonstrates both the equitable treatment of attribute groups and the preservation of vanilla data matching optimization. Experimental results on C-MNIST, C-FMNIST, CIFAR10-S, and CelebA datasets provide both quantitative and qualitative evidence of the method's effectiveness in mitigating unfairness compared to naive matching approaches.

strengths:
1. This is the first work to identify attribute unfairness in current data distillation methods.

2. The authors propose a simple yet effective method to solve this unfairness.

3. The authors provide theoretical justification for their proposed method in addressing the unfairness issue.

4. The authors present comprehensive experiments to demonstrate the soundness of their proposed method.

5. The paper is well-written and easy to follow.

weaknesses:
1. Including Vision Transformer architectures as backbone networks would further demonstrate the method's generalizability.

2. Examining its performance on more challenging datasets like CIFAR-100 or ImageNet would strengthen its practical applicability.

3. Including visual examples from the CelebA dataset in the supplementary material would help readers better understand the fairness improvements achieved by the proposed method.

4. The term "adversarial matching" could benefit from additional clarification, as the current mathematical formulation doesn't explicitly show adversarial operations. A brief explanation of this terminology would enhance the paper's clarity.

questions:
Please see the weaknesses
</content>
</thread_object>

</thread>
<thread>
creation date:
1730645324761

thread_type:
official_review

<thread_object>
creator: reviewer_j9j1

<content>
summary:
This paper addresses a critical issue in dataset distillation (DD), where biases inherent in original datasets tend to amplify within condensed datasets, often exacerbating unfairness toward minority groups. The authors propose "FairDD," a fair dataset distillation framework designed to mitigate these biases by integrating adversarial matching for protected attribute (PA)-wise groups, such as gender and race. Unlike conventional DD approaches that indiscriminately align to the entire dataset distribution (often skewed by majority groups), FairDD aligns synthetic datasets with specific PA groups, aiming to prevent dominance by majority representations. This targeted approach allows for more balanced synthetic data generation, maintaining classification accuracy while improving fairness. The paper’s theoretical analyses and extensive experiments show that FairDD outperforms traditional DD methods in fairness metrics without compromising on accuracy.

strengths:
Novel Approach to Fairness in Dataset Distillation: The concept of adversarially aligning synthetic datasets with PA-wise groups is an innovative approach that tackles a commonly overlooked issue in DD. FairDD’s adversarial matching mechanism, focusing on each PA group rather than the overall distribution, is a thoughtful solution that could inspire further research in fair dataset synthesis.

Versatility Across Matching-Based DD Methods: The proposed FairDD framework is adaptable to various matching-based DD methods, as shown by its successful application in both Distribution and Gradient Matching methods. This versatility highlights FairDD’s potential as a generally applicable solution in the DD field.

Extensive Theoretical and Experimental Validation: The authors conducted thorough theoretical analyses to support their framework, providing a solid foundation for understanding why FairDD effectively reduces biases in dataset distillation. Additionally, they validated their approach through extensive experiments, consistently showing improved fairness across different DD methods without sacrificing model accuracy. This combination of theoretical and empirical rigor adds credibility to FairDD's effectiveness and reliability.

Maintaining Accuracy while Enhancing Fairness: An important advantage of FairDD is that it achieves fairness without compromising the target classification performance, addressing a common trade-off in fairness-oriented methods.

weaknesses:
Limited Practicality Discussion: While FairDD’s focus on fairness is commendable, the framework’s real-world applicability could be affected by computational demands introduced by adversarial matching. The authors could discuss the added computational overhead and resource requirements, especially when scaling to larger datasets.

Scalability to Other Protected Attributes: The paper primarily discusses FairDD’s effectiveness concerning attributes like gender and race. However, it is unclear how well this method generalizes to other protected attributes or more nuanced groups within a PA, especially when there are multiple attributes with intersecting biases. An exploration of such scenarios would enhance the robustness of the approach.

Potential Dependency on Original Dataset Quality: The framework assumes that PA groups in the original dataset are balanced enough to train FairDD effectively. In real-world applications, where some PA groups might be underrepresented, this assumption could limit FairDD’s effectiveness. The authors could address how FairDD performs under different levels of dataset imbalance.

Theoretical Justification: While the paper provides theoretical analysis, a deeper exploration of why adversarial matching effectively reduces bias in condensed datasets could strengthen the contribution. Further theoretical insights could add rigor and help clarify the underlying mechanisms driving FairDD’s success.

questions:
Good paper. I have no further questions
</content>
</thread_object>

</thread>
<thread>
creation date:
1730659790973

thread_type:
official_review

<thread_object>
creator: reviewer_pzx9

<content>
summary:
The paper addresses a crucial issue: fairness in dataset distillation. The first part of the paper shows that if the original dataset is biased, the distilled dataset exacerbates such biases by generating images primarily from the majority group. Thereafter, the paper proposes a simple modification to the loss function to ensure representation from the different groups in the condensed dataset. The authors have shown theoretical evidence for the efficacy of their method and demonstrated its effectiveness across multiple datasets of varying versions. Overall, this is an issue that demands more research and requires further analyses to ensure that the condensed dataset is fair.

strengths:
1. The loss function FairDD seems intuitive. Instead of optimizing across all samples of a given class, the authors have ensured that each group in the training set gets a fair chance in pulling the condensed dataset towards itself.
2. The analysis on the exacerbation of biases in the distilled dataset is a strong evidence towards the necessity behind further research on this issue.
3. The paper has provided theoretical analysis for their proposed approach.
4. The authors have performed their analysis on a variety of datasets and across multiple model architectures.

weaknesses:
1. For the ColorMNIST and CelebA, the DEO_M is often comparable to that of the original dataset, showing that the distilled data still may follow the bias of the original dataset, though it hasnt alleviated the bias.
2. One big issue is that it is not clear if the reported scores are statistically signifcant as no std was reported.
3. The only real image dataset for which the analysis was done is CelebA.

questions:
1. The proposed loss function currently considers all groups, but does not consider their cardinality. Would upweighting the minority groups benefit the loss further, where the weights can be inversely proportional to the group size?
2. How robust is this method to the availability of the spurious/group labels? E.g., if a method like JTT [a] is employed to get pseudo labels for the bias attribute, how would the performance change in terms of fairness?
3. What if the original dataset is group balanced first, and then the traditional distillation losses are applied? Would that automatically help reduce the bias?
4. The target attributes reported for CelebA are either attractive, big nose, or young. Attractive and big nose can be subjective. Does the efficacy of the proposed method hold for a more objective attribute like blond hair, which is a famously reported in the fairness literature?


        [a] Liu, Evan Z., et al. "Just train twice: Improving group robustness without training group information." International Conference on Machine Learning. PMLR, 2021.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730917376814

thread_type:
official_review

<thread_object>
creator: reviewer_yauw

<content>
summary:
This paper proposes the idea of fair dataset distillation by ensuring that the protected attribute-based samples provide uniform signals across the groups. This is ensured using a distribution matching objective, uniformly distributed across the protected groups. The authors show results on various benchmarks including synthetic and real-world (CelebA) datasets.

strengths:
- The paper is well-written and clear.
- The experimental results provided are extensive and show improvements.

weaknesses:
- Novelty: The biggest concern for me is the novelty. The idea of class-weighting proposed in this work is classically used in class-imbalanced problems. Further, it has been used in the fairness scenarios as well by multiple works [R1, R2]. Although the results are improved it’s hard to see the ingenuity in the approach, it would be great if the authors could please clarify the differences.


- Adversarial Reference is Vague: The authors mention the formulation to be adversarial, however, the loss doesn’t seem to have an explicit adversarial component. Hence, it would be great to clarify the objective clearly concerning the min-max loss component. 


- Theory: Theorem 4.2 is a variant of weighted ERM kind of results, which shows that the weighted loss could be an upper bound to standard ERM kind of loss [R3]. Such weighted results have been extensively studied earlier in literature, hence it’s hard for me to realize the potential of the new results.


- Related Baseline Missing: The following paper [R2] introduces the idea of weighted ERM for fairness with protected sub-groups, using loss weighting and escaping saddle points via SAM [R2, R4] to improve fairness properties. Due to the overlap of the current problem with this, it’s important to compare or contrast the proposed method with a baseline constructed on the basis of these.

[R1] Fairness-aware Class Imbalanced Learning
[R2] Fairness-Aware Class Imbalanced Learning on Multiple Subgroups
[R3] Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling
[R4] Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data

questions:
Is the weighted centroid matching objective in Eq. 4 correct? It might be insufficient, as two groups of samples with entirely different distributions can still have the same centroid. Could you clarify the distribution matching objective better?
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</thread>
<thread>
creation date:
1732267494094

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General Response

comment:
Dear Reviewers and ACs,

We very much appreciate the insightful and detailed review. We are excited to hear the encouraging comments, particularly that our work `for the first time revealed and addressed the crucial issue of bias inheritance and exacerbation in dataset distillation` that was previously neglected (Reviewer **PZx9** and **j9J1**), and "`inspired the research of fairness dataset synthesis`" (Reviewer **PZx9** and **j9J1**). We are also pleased to hear the positive feedback from all the reviewers, including "`novel approach`" (Reviewer **j9J1**), "`theoretical foundation and comprehensive empirical validation`" (Reviewer **j9J1**, **SjG8**), and "`well-written and easy to follow`" (Reviewer **Yauw** and **SjG8**). While we responded to each of the reviewer comments individually, we also provide a brief summary of the main contents of the rebuttal in response to the reviews:

- In response to the feedback from Reviewer **Yauw** and **SjG8**, we have supplemented the relevant clarification at line 199 in the revised version.

- As recommended by Reviewer **j9J1**, we have supplemented more analysis of computation overhead from input resolution and sample number to provide more practicality discussion.

- To address the concerns raised by Reviewers **Yauw** and **PZx9**, we have added more experiments about the weighting mechanism for different groups.

- We have also addressed the feedback from Reviewers Yauw and PZx9 by incorporating
  important references [1,2,3,4,5] that were previously missing.

We also provide a summary of the main changes made to the revised version of the paper in response to the reviews:

- For Reviewer **PZx9**, we update the reported results with standard deviation in all tables of the main text (`Tables 21-25`). An additional experiment on the common real dataset UTKFace has been added in `Table 11`. Analysis of other target attributes, noise group labels, and balanced original dataset have been supplemented in `Tables 7, 12, and 14`.
- For Reviewer **j9J1**, we supplement the experiment about additional computation overhead in `Table 10`, more protected attributes in `Table 7`, fine-grained group division in `Table 15`, and group underrepresentation in `Table 16`.
- For Reviewer **SjG8**, `Table 17` is added to respond to the exploration of ViT backbone. Exploration of the challenging dataset is presented in `Table 18`. `Figure 13` is supplemented to present the visualization of CelebA. 

`We have uploaded the revised paper which includes additional experiments and illustrations to address the feedback from the reviewers. For ease of review, we highlight the revised text in orange. For other questions raised by the reviewers, please see our response to individual questions and concerns below each review.`

> **Reference:**

> [1] Subramanian S, Rahimi A, Baldwin T, et al. Fairness-aware class imbalanced learning[J]. arXiv preprint arXiv:2109.10444, 2021.

> [2] Tarzanagh D A, Hou B, Tong B, et al. Fairness-aware class imbalanced learning on multiple subgroups[C]//Uncertainty in Artificial Intelligence. PMLR, 2023: 2123-2133.

> [3] Vogel R, Achab M, Clémençon S, et al. Weighted empirical risk minimization: Sample selection bias correction based on importance sampling[J]. arXiv preprint arXiv:2002.05145, 2020.

> [4] Rangwani H, Aithal S K, Mishra M. Escaping saddle points for effective generalization on class-imbalanced data[J]. Advances in Neural Information Processing Systems, 2022, 35: 22791-22805.

> [5] Liu, Evan Z., et al. "Just train twice: Improving group robustness without training group information." International Conference on Machine Learning. PMLR, 2021.
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Metareview:
metareview:
This submission was reviewed by four reviewers, with discussions involving multiple rounds of clarifications between the reviewers and authors. The opinions were mixed: two reviewers viewed the paper as a borderline case, leaning towards acceptance, while the remaining two reviewers held contrasting accept and reject stances. Overall, the final scores converged near a borderline rating.

While one reviewer provided a high positive score, the review lacked technical depth. The critical reviewer, however, raised significant concerns, including the limited novelty of FairDD compared to existing class-imbalanced learning methods and prior works addressing biases in dataset distillation. Additionally, several reviewers noted that the term "Adversarial Matching" used in the paper is misleading.  Another concern is that the paper assumes the availability of group labels for fair dataset distillation. However, it does not include a baseline comparison where the classifier used in the distillation process is itself trained fairly using established approaches from the literature. This raises an important question: will the distilled dataset  ensure fairness, or will it still exhibit biases? This issue remains unexplored and warrants further experimentation.

After a thorough examination of the reviews and rebuttal, the AC panel concluded that the paper requires significant revisions to address these issues. While it shows potential, the current version does not meet the bar for acceptance. We encourage the authors to incorporate the reviewers' feedback to strengthen the work for future submissions.