Learning Fairer Representations with FairVIC
Charmaine Barker, Daniel Bethell, Dimitar Kazakov
TL;DR
FairVIC is a novel deep learning method that improves fairness by incorporating variance, invariance, and covariance into the loss function, achieving significant bias reduction without sacrificing accuracy.
Contribution
This paper introduces FairVIC, a new approach that enhances fairness in neural networks by abstracting fairness concepts and integrating specific statistical terms into the training loss.
Findings
Achieves approximately 70% improvement in fairness metrics
Maintains accuracy while significantly reducing bias
Effective across diverse datasets and fairness criteria
Abstract
Mitigating bias in automated decision-making systems, particularly in deep learning models, is a critical challenge due to nuanced definitions of fairness, dataset-specific biases, and the inherent trade-off between fairness and accuracy. To address these issues, we introduce FairVIC, an innovative approach that enhances fairness in neural networks by integrating variance, invariance, and covariance terms into the loss function during training. Unlike methods that rely on predefined fairness criteria, FairVIC abstracts fairness concepts to minimise dependency on protected characteristics. We evaluate FairVIC against comparable bias mitigation techniques on benchmark datasets, considering both group and individual fairness, and conduct an ablation study on the accuracy-fairness trade-off. FairVIC demonstrates significant improvements () in fairness across all tested metrics…
Peer Reviews
Decision·ICLR 2025 Conference Withdrawn Submission
- The method is broadly applicable to reduce a variety of biases at the same time - Weights on new loss terms can be dynamically balanced - The additional loss terms do not introduce a large computational overhead on the forward or backward passes
See questions
1. This paper introduces a novel fairness regularizer that simultaneously optimizes for both group and individual fairness. 2. The proposed method is evaluated across two modalities, demonstrating optimal performance in terms of the fairness-accuracy trade-off when compared to baseline methods. 3. Additionally, the paper presents a method for automatically tuning the weights of the regularizer.
1. The evaluation is limited by the number of datasets used. It would be beneficial to test the proposed method on a wider range of tabular and text classification datasets [1, 2, 3]. 2. FairVIC shares similarities with both fair representation learning methods and fairness penalty approaches; however, the authors only compare it with the adversarial debiasing algorithm by Zhang et al. It would be helpful to compare FairVIC with other fairness regularizers, such as those addressing equalized od
The authors propose fairness regularizers that aim to satisfy notions of group, individual, and counterfactual fairness simultaneously For the chosen datasets, the method is evaluated extensively against the chosen baselines. While there are a few additions and ablations I hope to see, the results are already considerable. I like the visualization provided by Figure 1.
The introduction of the regularizers is somewhat instructable. Basic questions like how do they regularizers depend on the model’s predictions are left unanswered. The dismissal of prior approaches (e.g. regularizing using a metric grounded in legal precedent, post-processing methods at large) was rather uncharitable, in my opinion. What the authors do is describe potential limitations of these methods, but I think the claims are not properly substantiated, and a more detailed account for how t
This method is straightforward and comprehensible. The writing style is clear and easy to follow. Figure 1 effectively illustrates the distinctions among various methods.
**Technical flaw:** My primary concern pertains to this point. The author introduces three loss terms in equations (1), (2), and (3). However, terms (1) and (2) *have no direct dependence on the network parameters*. Therefore, the gradient of the parameters with respect to those losses is always zero, making optimization of these terms nonsensical. This constitutes a significant technical flaw. **Lack of evaluation:** Given the plethora of in-processing methods available in fairness and robust
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Neural Networks and Applications