Fair4Free: Generating High-fidelity Fair Synthetic Samples using Data-Free Distillation

Authors: Md Fahim Sikder, Daniel de Leng, Fredrik Heintz
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: data fairness, fair generative models, knowledge distillation, latent space distillation, synthetic data, biased data

Abstract:
This work presents Fair4Free, a novel generative model to generate synthetic fair data using data-free distillation in the latent space. Fair4Free can work on the situation when the data is private or inaccessible.  In our approach, we first train a teacher model to create fair representation and then distil the knowledge to a student model (using a smaller architecture). The process of distilling the student model is data-free, i.e. the student model does not have access to the training dataset while distilling. After the distillation, we use the distilled model to generate fair synthetic samples. Our extensive experiments show that our synthetic samples outperform state-of-the-art models in all three criteria (fairness, utility and synthetic quality) with a performance increase of 5\% for fairness, 8\% for utility and 12\% in synthetic quality for both tabular and image datasets.

Decision: reject
Reviewer scores: 3, 3, 3, 3

Reviews and discussion:
<thread>
creation date:
1729024449573

thread_type:
official_review

<thread_object>
creator: reviewer_z6so

<content>
summary:
This work proposes a generative model for fair synthetic data generation with data-free distillation. The proposed method involves first training a fair teacher model on the biased dataset and producing a fair representation, then distil using a student architecture. Finally, synthetic fair samples are reconstructed from the distilled fair representation. The authors apply the method on image and tabular data, and outperform all baseline methods.

strengths:
1. The empirical results look promising as they outperform most baselines.
2. The experiment is performed on both visual and tabular data 
3. The proposed method is well presented and easy to understand

weaknesses:
1. The teacher model trained in this paper is assumed to have fair representation. This involves using a regularized distance correlation minimization loss to weaken the connection between the sensitive and non-sensitive attributes. This limits the applicability of the proposed method (i.e. distilling from pre-trained large generative models without a fairness guarantee).
2. Experiment on visual data (i.e. CelebA and Colored-MNIST) has no quantitative benchmarking and comparison with baseline methods. Comparison with baseline such as EDM [1] and showing better fairness can strength the soundness of the experiment, even without outperforming it in terms of visual quality.
3. The claim about data-free training is a bit confusing; please refer to the Questions section.

[1] Karras, T., Aittala, M., Aila, T. and Laine, S., 2022. Elucidating the design space of diffusion-based generative models. Advances in neural information processing systems, 35, pp.26565-26577.

questions:
I'm a bit confused about the motivation for using data-free distillation. The authors claim that the method's benefit is that it can be applied when data is inaccessible; however, the teacher model required in the method is trained with a designed loss function to have a fair representation. 

I assume the underlying assumption of this work is that the existing foundational generative models do not have the desired fairness property, so in reality, the original training data is still required to apply the proposed method. Then, my question is, why does data-free distillation matter in this case? Would it be easier to use a single model to learn how to generate fair data from scratch?

As a non-practitioner in fairness, I would appreciate if the author could clarify if I misunderstood the setting or the motivation of the work.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730481272144

thread_type:
official_review

<thread_object>
creator: reviewer_emfz

<content>
summary:
Fair4Free presents a novel approach to generating synthetic data that is fair and unbiased using data-free distillation. This method is particularly useful when access to training data is restricted due to privacy concerns. The process involves distilling knowledge from a pre-trained teacher model to a smaller student model without using real data, relying instead on noise input. The paper claims significant improvements over other models in terms of fairness, utility, and synthetic quality.

strengths:
1.  The data-free approach allows the generation of fair data even when access to original datasets is restricted, addressing significant privacy concerns.
2.  By using a smaller student model, Fair4Free reduces computational resources and enables potential deployment on edge devices.
3.  The model demonstrates superior performance across fairness, utility, and synthetic quality metrics compared to state-of-the-art models.

weaknesses:
1. Complexity and Overhead: While reducing computational costs via a smaller model, the initial setup of training and distilling between models can be complex and resource-intensive.
2.  How does the performance of synthetic samples generated by Fair4Free compare to non-synthetic data in terms of real-world utility and fairness?
3. How robust is Fair4Free to shifts in data distributions that might occur in practical scenarios?

questions:
see weaknesses.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730481823100

thread_type:
official_review

<thread_object>
creator: reviewer_9nci

<content>
summary:
Fair4Free introduces an innovative generative model designed to create fair synthetic data without accessing actual datasets. It leverages a technique called data-free distillation, where knowledge is transferred from a teacher model to a smaller student model using only noise as input. The approach is touted for its effectiveness in generating data that adheres to fairness, utility, and quality benchmarks, surpassing existing models.

strengths:
1. Enhanced Privacy Protection: The model's ability to operate without real data makes it highly suitable for environments with strict privacy regulations or sensitive data restrictions.
2.  By employing a smaller model architecture, Fair4Free minimizes the computational demands, making it feasible for deployment on less powerful devices, including edge devices.
3. It consistently outperforms other models in generating synthetic data that scores highly on fairness, utility, and quality metrics, as validated by rigorous experimental evaluations.

weaknesses:
1.  The model currently focuses on addressing bias with respect to single sensitive attributes, potentially overlooking complex bias scenarios involving multiple intersecting attributes.
2. Scalability Concerns: While the model is efficient, scaling it to handle larger or more complex datasets without compromising performance remains a challenge.
3. Can Fair4Free be adapted to efficiently manage multiple sensitive attributes to tackle intersectional biases more effectively?
4. In terms of decision-making and predictive accuracy, how do the synthetic datasets generated by Fair4Free compare to those derived from traditional data generation methods?
5. Adaptability to Data Shifts: What measures can be taken to enhance Fair4Free's robustness against dynamic changes in data distribution that are common in real-world settings?

questions:
See weaknesses.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730709233366

thread_type:
official_review

<thread_object>
creator: reviewer_lgey

<content>
summary:
The authors propose Fair4Free, a novel generative approach for generating high-fidelity synthetic fair data using knowledge distillation.
Their proposed method consists of three stages: 1) train a VAE on the biased dataset to learn a fair representation, 2) use it as a teacher model and distill the fair representation to a student model, and 3) use the trained VAE decoder and student model to reconstruct high-fidelity fair synthetic samples. They show experiment results on tabular and image datasets.

strengths:
* The paper addresses an important problem of generating synthetic fair data.
* The experiments include a wide range of evaluation metrics to thoroughly evaluate the proposed method.

weaknesses:
* The contributions of this paper are unclear. Although the authors claim the method is *data-free*, it still relies on a biased dataset to train the teacher model in the first stage. Furthermore, the method combines the student encoder with the teacher decoder to generate synthetic samples. Then why not simply use the teacher model directly? What advantages are gained by distilling the teacher encoder into a smaller student encoder only to recombine it with the teacher decoder? The paper also lacks an ablation study to justify the design choices in the method.
* Overall, the paper needs substantial improvement in writing quality and clarity. Implementation details are severely lacking, e.g., hyperparameters used for training the proposed and compared methods, details on synthetic data generation during evaluation, how the random forest model is trained, and explanation of the evaluation metrics (e.g., DPR, EOR).
* The method demonstrates minimal performance gains over the compared methods across downstream tasks (Table 2,3).
* They should additionally report FID for synthetic data quality.

questions:
* The paper needs to improve the clarity of implementation details and evaluation protocols mentioned above.
* The paper needs an ablation study to justify the design choices of the method.
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</thread>


Metareview:
metareview:
The submission proposes "Fair4Free," a generative approach to creating synthetic fair data via data-free distillation. While the paper addresses an important issue in fairness and privacy, it fails to convincingly justify its contributions and practical advantages. Below is a summary of the key feedback:

Strengths:
* Addresses a relevant problem of fairness in synthetic data generation.
* The approach operates in privacy-restricted scenarios, which is a valuable consideration.
* Includes evaluation across multiple metrics on tabular and image datasets.

Weaknesses:
* Unclear Contributions: The method's claim of being "data-free" is misleading as it still relies on a biased dataset to train the teacher model. Reviewers questioned the necessity of distilling knowledge from the teacher model rather than using it directly. The novelty and advantage of the distillation process, particularly when reusing the teacher model’s decoder, are insufficiently justified.
* Limited Empirical Validation: Quantitative results demonstrate marginal improvements over baselines, and the performance gains (5%-12%) are not compelling. Key experimental details, such as hyperparameters and ablation studies, are missing, undermining the reproducibility and validation of design choices. Evaluation on image datasets (e.g., CelebA, Colored-MNIST) lacks robust benchmarking against advanced baselines (e.g., diffusion models).
* Presentation and Clarity: The writing lacks clarity, and the methodology is not well-explained. Several questions from reviewers (e.g., on teacher-student architecture rationale and multi-attribute fairness) remain inadequately addressed.
* Scalability and Applicability: The approach struggles with scaling to larger datasets or complex bias scenarios, limiting its generalizability.
Intersections of multiple sensitive attributes, an essential aspect of fairness, are not addressed.

Based on the unclear novelty, lack of robust validation, and insufficient responses to reviewer concerns, I recommend rejection. While the paper has potential, it requires significant improvements in methodology, experimental rigor, and presentation to meet the standards of acceptance.