Title: Fair Representation Learning with Controllable High Confidence Guarantees via Adversarial Inference
Venue: NeurIPS 2025
Accepted: yes
Keywords: Fair Representation Learning

Statistical Guarantees

Controllable Guarantees
Abstract: Representation learning is increasingly applied to generate representations that generalize well across multiple downstream tasks. 
Ensuring fairness guarantees in representation learning is crucial to prevent unfairness toward specific demographic groups in downstream tasks.
In this work, we formally introduce the task of learning representations that achieve high-confidence fairness. 
We aim to guarantee that demographic disparity in every downstream prediction remains bounded by a *user-defined* error threshold $\epsilon$, with *controllable* high probability.
To this end, we propose the ***F**air **R**epresentation learning with high-confidence **G**uarantees (FRG)* framework, which provides these high-confidence fairness guarantees by leveraging an optimized adversarial model. 
We empirically evaluate FRG on three real-world datasets, comparing its performance to six state-of-the-art fair representation learning methods. 
Our results demonstrate that FRG consistently bounds unfairness across a range of downstream models and tasks.


Peer reviews:
comments: SUMMARY:
The paper proposes FRG, a framework for fair representational learning that guarantee specific high confidence fairness bounds for any downstream task. The works achieves this by proposing a adversarial inference component and statistic fairness test.

STRENGTHS AND WEAKNESSES:
## Strength

1. The authors addresses a gap in the research field, indicating that existing methods provide empirical or estimates fairness guarantees without formalized proof.

2. The authors' theoretical proof between demographic parity and covariance seems sounds, with reasonable foundation for adversarial inference

3. The author provides sufficiently strong empirical evaluation 

## Weakness 

Although I am not a expert in this field:

1. The authors strong emphasis on demographic parity seems limiting, noting that there exist other popular fairness measurements such as equal opportunity and equalized odds (multiple fairness measurements are often considered in the fairness measure literature for such works). How does this work fair for other fairness measurements?

2. The authors proposes a strong IID assumption of the data. Although the assumption is fair for theoretical study, I am concern on the distribution shift between training and deployment. More empirical test could be provided to address this concern.

3. The authors proposed solution requires a multi-stage pipeline, which could potential be very computational heavy for large and datasets and complex task. How scalable is such a solution?

4. Additionally, how should one consider the thresholds $\epsilon$ and $\delta$?

QUESTIONS:
Please see the question in the weakness field (included for clarity).

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces Fair Representation learning with high confidence Guarantees(FRG), a framework that learns fair representations with high confidence. The fairness tolerance and confidence level are user-specified. The framework provides this guarantee for any downstream task and model that uses the learned representation. FRG splits the training dataset into a candidate-selection set and a fairness-test set. Then, FRG uses the candidate selection set to train a representation learning model. The paper utilizes the VAE-style objective and adds a fairness proxy constraint to ensure the learned representations satisfy the fairness constraint. After the candidate model is provided, FRG trains an adversarial model that tries to estimate sensitive attributes using the learned representation. The authors show that the adversary that maximizes the covariance between prediction and sensitive attribute is equivalent to maximizing the demographic parity(DP) gap. Lastly, the framework uses the fairness-test set to provide confidence bounds on the worst-case DP gap using the Student t-test intervals. If the test fails, the framework returns that no solution was found(NSF). The experiments cover multiple real-world datasets, multiple tasks, and comparisons with baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- To the best of my knowledge, this is the first fair representation learning method offering user-controllable fairness tolerance and confidence certification on any downstream task that is required to be DP fair.\n- The experiments section is quite comprehensive, the authors compare their method with multiple baselines and provide enough ablation studies. Their method performs better than the baselines in terms of satisfying the fairness violation, and the AUC and accuracy values are similar to the baseline models. They also report the frequency of returning NSF.\n- The theoretical results are supported by clear assumptions and easy-to-follow proofs.\n\nWeaknesses\n- The adversarial inference utilizes the relationship between the DP gap and the covariance between the predicted sensitive attribute and the true sensitive attribute. It is not clear to me how this will generalize for other fairness notions, such as equal opportunity or equalized odds.\n- The student-t confidence bound relies on CLT; there is no study on how this affects small per-group sample sizes. The CLT approximation could behave poorly. \n- In the experiments section, the FRG and baseline methods use the same number of training samples. The baselines use another dataset for validation, but FRG uses this dataset for both validation and fairness testing. I think that for a fair comparison, the fairness-test dataset should be picked among the training samples. \n\nOther comments:\n- The paper is well written, except for a few parts. I think that the section confidence upper bound on fairness violation(5.2.1) is a bit confusing; the pairing part can be explained better. Also, the part where the confidence bound hyperparameter for training, $\\alpha$, can be explained better, is it just the previous $\\hat{U}=\\alpha U$?\n- Line 206, mulitclass --> multiclass\n- Line 259, hyperparamtr --> hyperparameter\n- Line 648, reference to equation 32 doesn't look correct to me.\n\nQUESTIONS:\nPlease see the weaknesses section.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Fair Representation learning with high confidence Guarantees (FRG), which is an algorithm for learning models that are guaranteed to be fair in terms of demographic parity when applied to downstream tasks with high probability. The high-level idea applied by FRG is to first train a candidate model (e.g. a variational autoencoder modified with a fairness constraint) using part of the data and then train an adversary that aims to predict protected attributes using the representations from the candidate model. The adversary is used to check whether the candidate model provides the guarantees specified by the user. More specifically, the adversary is used to estimate the fairness bounds based on small data samples (data pairs) using a statistical test (e.g., student-t). Under some assumptions on the data samples, the number of samples, and the accuracy of the adversary, the paper shows that the theoretical guarantee on fairness is achieved. In the experiments, FRG is compared against six baselines in terms of downstream fairness and accuracy using three datasets. The results show that FRG achieves better accuracy than alternatives that are able to guarantee downstream fairness with high probability.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n- The paper addresses a relevant problem.\n- The paper is well-written and its motivation is clear.\n- The experimental results support the main conclusions in the paper.\n\n**Weaknesses:**\n- The accuracy of the proposed approach seems too low when fairness bound is tight\n- There is little discussion about the assumptions of the proposed model\n- It is not clear how theorem 5.2 is used in the paper (compared to DP)\n\n**Detailed comments:**\n\nThis is an interesting paper addressing a relevant problem but with some clarifications needed regarding its assumptions and experiments. I will detail my main concerns here.\n\n*Accuracy:* The accuracy results for FRG reported in Figure 2 seem very low when epsilon is small. This might be a limitation of the dataset, the model, or it could be justified theoretically. It would be helpful to understand the source of the error to some extent. In particular, one can assume that there are no practically useful models that can provide the type of guarantees considered in this paper. \n\n*Assumptions:* The main assumptions of the proposed approach are listed in the limitations but some discussion is needed regarding how realistic these assumptions are and whether they are expected to hold for the datasets considered. In particular, the assumption of an optimal adversary seems unrealistic, so a stronger result would bound the bounds in terms of how close the adversary is to optimal or in terms of the adversary model and the data used to train it. A synthetic dataset could be useful to clarify this.\n\n*Theorem 5.2:* It was not clear to me why Theorem 5.2 is useful in this paper. Does it provide any benefit compared with the original definition of Demographic Parity?\n\nQUESTIONS:\nQ1. Are there non-trivial fairness vs. quality tradeoffs with the guarantees provided in the paper? Is it possible to provide a more comprehensive view of such tradeoffs to demonstrate the practical use of the proposed solution?\n\nQ2. How realistic are the assumptions considered in the paper and how robust is the method to small violations of such assumptions that might occur in real datasets.\n\nQ3. How is Theorem 5.2 useful in the paper?\n\nLIMITATIONS:\nThe limitations are discussed in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper formally introduces the task of learning representations that achieve high-confidence fairness. Then it presents a novel framework FRG that learns fair representations for all downstream tasks based on demographic parity.  It guarantees with high confidence that the output representation models are fair according to user-specified thresholds of unfairness and confidence levels. Experiments on three real-world datasets demonstrate that FRG consistently bounds unfairness across a range of downstream models and tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- This paper identifies a practical gap, a lack of high-confidence guarantees in current fair representation learning approaches. And it extends fairness guarantees to all downstream tasks and models. The studied problem is interesting and significant.\n- Experiments on the three datasets and a range of tasks demonstrate the effectiveness of the proposed framework in both fairness and useful representation learning.\n- The paper is clearly written and easy to follow. \n\nWeaknesses:\n- The theoretical analysis in section 5.2.3 assumes access to an optimal adversary. Although FRG approximates the adversary, it might not always be realistic in practice. Is there theoretical support for the guarantee of obtaining the optimal adversary, such as requirements for model complexity and training data size?\n- Repeated training of adversaries and statistical testing introduces a computational burden. While not prohibitive, this could limit real-world scalability. The author should also present a comparison of the computation cost/time.\n- The presentation of the experimental results is poor. In Figure 3, I can hardly see the blue line, which is the performance of the proposed method.\n- The choice of datasets is limited, and some of them are outdated. For the adult dataset, you should better use this updated one: \"Retiring Adult: New Datasets for Fair Machine Learning\"\n\nQUESTIONS:\nBesides the questions in the Weaknesses, I also have the following questions:  \n- Does the proposed method apply to other supervised fairness metrics, such as equal opportunity and equalized odds?\n- What are the computational tradeoffs? Can you compare training time with otherbaselines?\n- The details of the model architecture and parameter settings are missing in the paper.\n- In line 189, you mentioned \"In practice (section 6), we find it sufficient to train an approximately optimal adversary to predict S based on Z\". Can you elaborate more? Does the choice of adversary design affect this statement?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}