The paper proposes a fairness-aware Mixture-of-Experts (MoE) framework that tackles routing-induced bias by applying subgroup reweighting to correct data imbalance and gate entropy regularization to prevent the gating network from collapsing onto subgroup attributes. This end-to-end approach keeps expert utilization balanced and interpretable, offering a clear view of how subgroups are allocated across experts. Experiments show that the method improves fairness while maintaining competitive predictive performance.
By Sunhee Hwang
arXiv:2510. 23469v2 Announce Type: replace Abstract: Self-supervised pre-training on unlabeled graph data has become a common paradigm for Graph Neural Networks (GNNs).
By Yuhan Yang, Xingbo Fu, Jundong Li
arXiv:2608. 19381v1 Announce Type: cross Abstract: Network embedding methods learn low-dimensional representations of graph-structured data to support downstream tasks such as node classification, link prediction, and influence maximization.
By Ella Has, Harshith Kumar Yadav, Gaurav Dixit, Mykola Pechenizkiy, Akrati Saxena
Subgraph Filtering for Fair Graph Neural Networks (SF‑GNN) is a lightweight, architecture‑agnostic framework that reduces structural bias in GNNs by identifying and filtering bias‑prone edges during message passing. It combines sensitive homophily with structural propagation amplifiers such as hub participation and triadic closure to detect problematic edges, then applies stochastic edge filtering to downweight or remove them while preserving the rest of the graph. Experiments on five benchmark datasets demonstrate that SF‑GNN consistently improves fairness while maintaining competitive predictive performance, achieving a better fairness–accuracy trade‑off than recent fairness‑aware GNN baselines.
By Haohui Lu, jiyuan Tian, Fangyu Zhou, Shahadat Uddin
arXiv:2404. 01356v3 Announce Type: replace-cross Abstract: Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings.
By Xuran Li, Hao Xue, Peng Wu, Xingjun Ma, Zhen Zhang, Huaming Chen, Flora D. Salim
arXiv:2606. 09881v1 Announce Type: new Abstract: Deepfake detectors show large performance gaps across demographic groups.
By Ryan Brown, Chris Russell
arXiv:2606. 31704v1 Announce Type: cross Abstract: The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance disparities across demographic groups.
By Maxime Moussi, Beno\^it Ronval, Siegfried Nijssen, F\'elicien Schiltz
arXiv:2606. 20461v1 Announce Type: new Abstract: Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender.
By Bruno Scarone, Alfredo Viola, Ren\'ee J. Miller
arXiv:2504. 21296v2 Announce Type: replace Abstract: Graph learning has evolved into Augmented Graph Learning (AGL) by integrating specialized machine learning (ML) techniques.
By Renqiang Luo, Huafei Huang, Ziqi Xu, Xikun Zhang, Enyan Dai, Bo Yang, Feng Xia
Graph neural networks (GNNs) can exhibit unfair behavior even when sensitive attributes are excluded from node features, because graph topology and message passing propagate group-correlated signals u...
arXiv:2404. 01356v4 Announce Type: replace-cross Abstract: Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings.
By Xuran Li, Hao Xue, Peng Wu, Xingjun Ma, Zhen Zhang, Huaming Chen, Flora D. Salim
arXiv:2609.16321v1 Announce Type: cross
Abstract: Existing fairness analysis tools predominantly operate as post-training evaluation frameworks, requiring practitioners to complete the full model dev...
By Archit Rathod, Saeid Tizpaz-Niari