Latent Space Smoothing for Individually Fair Representations

Peychev, Momchil; Ruoss, Anian; Balunović, Mislav; Baader, Maximilian; Vechev, Martin

doi:10.1007/978-3-031-19778-9_31

Cited by 2 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, most existing fairness methods lack theoretical analysis and guarantees [3,20], meaning that they do not provide a practical guarantee, i.e., provable lower bounds on the fairness of model prediction. This is significant for determining whether to deploy models in practical scenarios [5,19,34,35].…”

Section: Introductionmentioning

confidence: 99%

Endowing Pre-trained Graph Models with Provable Fairness

Zhang,

et al. 2024

Proceedings of the ACM on Web Conference 2024

View full text Add to dashboard Cite

Pre-trained graph models (PGMs) aim to capture transferable inherent structural properties and apply them to different downstream tasks. Similar to pre-trained language models, PGMs also inherit biases from human society, resulting in discriminatory behavior in downstream applications. The debiasing process of existing fair methods is generally coupled with parameter optimization of GNNs. However, different downstream tasks may be associated with different sensitive attributes in reality, directly employing existing methods to improve the fairness of PGMs is inflexible and inefficient. Moreover, most of them lack a theoretical guarantee, i.e., provable lower bounds on the fairness of model predictions, which directly provides assurance in a practical scenario. To overcome these limitations, we propose a novel adapter-tuning framework that endows pre-trained Graph models with Provable fAiRness (called GraphPAR 1 ). GraphPAR freezes the parameters of PGMs and trains a parameter-efficient adapter to flexibly improve the fairness of PGMs in downstream tasks. Specifically, we design a sensitive semantic augmenter on node representations, to extend the node representations with different sensitive attribute semantics for each node. The extended representations will be used to further train an adapter, to prevent the propagation of sensitive attribute semantics from PGMs to task predictions. Furthermore, with GraphPAR, we quantify whether the fairness of each node is provable, i.e., predictions are always fair within a certain range of sensitive attribute semantics. Experimental evaluations on real-world datasets demonstrate that GraphPAR achieves state-of-the-art prediction performance and fairness on node classification task. Furthermore, based on our GraphPAR, around 90% nodes have provable fairness.

show abstract

Section: Introductionmentioning

confidence: 99%

Endowing Pre-trained Graph Models with Provable Fairness

Zhang,

et al. 2024

Proceedings of the ACM on Web Conference 2024

View full text Add to dashboard Cite

show abstract

GBMix: Enhancing Fairness by Group-Balanced Mixup

Hong,

Yoon,

Joo

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Mixup is a powerful data augmentation strategy that has been shown to improve the generalization and adversarial robustness of machine learning classifiers, particularly in computer vision applications. Despite its simplicity and effectiveness, the impact of Mixup on the fairness of a model has not been thoroughly investigated yet. In this paper, we demonstrate that Mixup can perpetuate or even exacerbate bias presented in the training set. We provide insight to understand the reasons behind this behavior and propose GBMix, a group-balanced Mixup strategy to train fair classifiers. It groups the dataset based on their attributes and balances the Mixup ratio between the groups. Through the reorganization and balance of mixup among groups, GBMix effectively enhances both average and worst-case accuracy concurrently. We empirically show that GBMix effectively mitigates bias in the training set and reduces the performance gap between groups. This effect is observed across a range of datasets and networks, and GBMix outperforms all the state-of-the-art methods.

show abstract

Latent Space Smoothing for Individually Fair Representations

Cited by 2 publications

References 39 publications

Endowing Pre-trained Graph Models with Provable Fairness

Endowing Pre-trained Graph Models with Provable Fairness

GBMix: Enhancing Fairness by Group-Balanced Mixup

Contact Info

Product

Resources

About