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Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information
Enyan Dai, Suhang Wang
TL;DR
GNNs can inherit and amplify sensitive-attribute bias, while existing fairness methods provide limited support for graph data and sparse sensitive labels. FairGNN estimates missing attributes and combines adversarial debiasing with a fairness constraint. Theory and real-world experiments report fairness improvements alongside high classification performance.
Problem
Fair GNN learning is challenging because graph structure can magnify bias, fairness methods largely target i.i.d. data, and sensitive-attribute annotations are limited.
Method
FairGNN estimates unavailable sensitive attributes with a GNN and uses adversarial debiasing plus a fairness constraint for node classification.
Results
FairGNN significantly alleviates GNN bias while maintaining high node-classification performance across real-world datasets.
Takeaways & Limitations
FairGNN provides a framework for fair node classification when only limited nodes have known sensitive attributes.
Takeaways & Limitations
The fairness analysis assumes estimator predictions are non-random and that the sensitive-attribute estimator and representation learner have independent parameters.
Abstract
from arXiv · showhide
Graph neural networks (GNNs) have shown great power in modeling graph structured data. However, similar to other machine learning models, GNNs may make predictions biased on protected sensitive attributes, e.g., skin color and gender. Because machine learning algorithms including GNNs are trained to reflect the distribution of the training data which often contains historical bias towards sensitive attributes. In addition, the discrimination in GNNs can be magnified by graph structures and the message-passing mechanism. As a result, the applications of GNNs in sensitive domains such as crime rate prediction would be largely limited. Though extensive studies of fair classification have been conducted on i.i.d data, methods to address the problem of discrimination on non-i.i.d data are rather limited. Furthermore, the practical scenario of sparse annotations in sensitive attributes is rarely considered in existing works. Therefore, we study the novel and important problem of learning fair GNNs with limited sensitive attribute information. FairGNN is proposed to eliminate the bias of GNNs whilst maintaining high node classification accuracy by leveraging graph structures and limited sensitive information. Our theoretical analysis shows that FairGNN can ensure the fairness of GNNs under mild conditions given limited nodes with known sensitive attributes. Extensive experiments on real-world datasets also demonstrate the effectiveness of FairGNN in debiasing and keeping high accuracy.
1 INTRODUCTION
The paper frames fair GNN learning as a challenge because graph bias can amplify societal discrimination while sensitive-attribute labels are scarce. FairGNN addresses this by estimating missing attributes and combining adversarial debiasing with fairness constraints, with theory and experiments supporting its effectiveness.
- Motivation: GNNs may inherit historical bias in sensitive attributes, while graph topology and message passing can magnify discrimination.Nodes with similar sensitive attributes are more likely to connect, concentrating propagated representations within subgroups.
- Challenges: Fair GNN development is difficult because sensitive-attribute labels are scarce and existing fairness methods largely target i.i.d. data.Only 14% of teen Facebook users reportedly publish complete profiles, illustrating the annotation shortage.
- Problem: The paper studies fair graph neural networks with limited sensitive information as a novel problem.The stated challenges are overcoming sensitive-attribute shortages and ensuring fairness in the GNN classifier.
- Approach: FairGNN estimates missing sensitive attributes, then uses an adversary and a fairness constraint to make predictions independent and invariant to those estimates.The estimator supplies noisy attributes for fair classification, while the two debiasing mechanisms stabilize fairness training.
- Evidence: Experiments demonstrate that FairGNN reduces discrimination while maintaining high node-classification accuracy.The paper also reports theoretical analysis showing fairness at the global minimum despite estimated sensitive attributes.
2 RELATED WORK
The related work positions GNNs as graph models built around neighborhood aggregation and distinguishes this paper from prior fairness research. FairGNN targets fair node classification with limited sensitive-attribute annotations rather than fair graph embeddings alone.
- Graph Neural Networks: GNNs generalize neural networks to graph data and include spectral-based and spatial-based families.GCN simplifies graph convolution, while GAT uses self-attention to weight neighborhood aggregation.
- Graph Bias: GNNs propagate node information through graphs, but homophilous sensitive-attribute connections can concentrate representations within subgroups and increase discrimination risk.This motivates studying fairness directly in GNNs rather than treating graph propagation as neutral.
- Fairness in Machine Learning: Existing fairness methods revise training data, attributes, representations, or learning objectives through pre-processing, in-processing, and post-processing approaches.The cited approaches include correcting labels, revising attributes, and other training-stage interventions.
- Positioning: The paper differs from prior graph-fairness work by focusing on fair node classification and limited sensitive-attribute annotations.Earlier graph methods addressed fair representations on plain graphs without node attributes.
3 PRELIMINARIES ANALYSIS
This section shows that graph structure and message passing can amplify discrimination in GNNs, then defines fair node classification with limited sensitive-attribute labels. It introduces the datasets, notation, fairness criteria, and preliminary model comparisons.
- 3.6 Problem definition: Only a small subset of nodes has known sensitive attributes, creating a practical constraint for fairness-oriented model revision or regularization.The paper notes that only 14% of teen Facebook users publicly provide complete profiles.
- 3.2 Datasets: The study evaluates Pokec-z, Pokec-n, and NBA graphs using separately sampled labeled and sensitive-attribute node sets, validation sets, and test sets.Pokec tasks predict users’ working fields with region as the sensitive attribute; NBA uses binarized nationality.
- 3.5 Discrimination in Graph Neural Networks: Graph structure and message passing can magnify bias because intra-group edges make same-attribute node embeddings closer after aggregation.This linking bias can make learned representations exhibit sensitive-attribute bias.
- 3.5 Discrimination in Graph Neural Networks: GCN and GAT outperform MLP in classification but perform significantly worse in fairness, indicating that graph structure can aggravate discrimination.The comparison includes MLP, graph-enhanced MLP, GCN, and GAT, each run five times.
- 3.6 Problem definition: The fair node-classification problem seeks predictions that maintain high accuracy while satisfying criteria such as statistical parity and equal opportunity.The setting assumes binary labels and binary sensitive attributes, with fairness assessed using ΔSP and ΔEO.
4 METHODOLOGY
FairGNN combines sensitive-attribute estimation, adversarial representation debiasing, and covariance regularization to learn fair node classifiers with limited sensitive labels. Its analysis links the adversarial objective to equal representation distributions and statistical parity under stated assumptions.
- Framework: FairGNN combines a GNN classifier fG, a GCN-based sensitive-attribute estimator fE, and an adversary fA.The estimator predicts unknown sensitive attributes, while the classifier performs node-label prediction.
- Sensitive-attribute estimation: The estimator predicts sensitive attributes for unlabeled nodes, producing estimated attributes that support fairness training across the graph.Estimated attributes are combined with known attributes before adversarial training.
- Adversarial debiasing: The adversary predicts known or estimated sensitive attributes from fG representations, while fG learns representations that make those predictions difficult.This minmax game targets fair representations rather than merely changing final predictions.
- Theoretical analysis: The adversarial objective reaches its minimum when p(h|ŝ=1) = p(h|ŝ=0), because Jensen-Shannon divergence is zero only for equal distributions.Under the stated assumptions, this equality yields statistical parity for the classifier.
- Theoretical analysis: FairGNN’s theoretical guarantee assumes conditional independence between estimated sensitive attributes and representations and a nonrandom sensitive-attribute estimator.Under these mild conditions, global convergence implies statistical parity, and the covariance regularizer is zero when prediction and estimated sensitive attributes are independent.
- Covariance constraint: A covariance constraint is added to fG outputs to help maintain fair predictions when adversarial training is unstable.The constraint minimizes the absolute covariance between predicted labels and estimated sensitive attributes.
5 EXPERIMENTS
Experiments evaluate FairGNN against GNN and fairness baselines, ablate its components, and vary available sensitive attributes and labels. FairGNN reduces discrimination while maintaining classification performance across these settings.
- Fair Classification on Graph: FairGCN and FairGAT perform close to the base GNNs in classification, unlike general fair-classification and graph-embedding baselines.The comparison evaluates fairness with ΔSP and ΔEO and classification with ACC and ROC AUC.
- Fair Classification on Graph: FairGCN and FairGAT obtain ΔSP and ΔEO values close to 0 under limited sensitive information, indicating that discrimination is basically eliminated.FairGAT is slightly better than FairGCN in fairness, potentially because learnable edge coefficients reduce weights of bias-carrying edges.
- Fair Classification on Graph: The experiments demonstrate that FairGNN achieves fair and accurate node classification across different amounts of sensitive attributes and labels.All experiments are repeated five times, with mean and standard deviations reported for the three datasets.
- Ablation Study: AUC 0.80 for the GCN sensitive-attribute estimator versus 0.69 for the MLP estimator, while the weaker estimator yields only slightly worse fairness.Removing the estimator produces a much larger ΔSP because the provided sensitive attributes are inadequate.
- Ablation Study: Both covariance constraint and adversarial debiasing improve fairness, but using them together achieves better fairness than either component alone.Adversarial debiasing regularizes node representations, whereas covariance constraint acts directly on predictions.
- Impacts of Sizes of V_S and V_L: FairGAT achieves very low ΔSP with as few as 200 nodes having sensitive attributes, while its classification-performance drop remains marginal across tested label quantities.Experiments vary |V_S| from 200 to 3000 and |V_L| from 500 to 2000 on Pokec-z.
6 CONCLUSION AND FUTURE WORK
The paper studies fair GNN learning with limited sensitive information and proposes FairGNN to address it. Theory and real-world experiments support reducing bias while maintaining node-classification performance, but future work must address inaccurate provided sensitive attributes.
- Conclusion and Future Work: FairGNN estimates missing sensitive attributes and uses adversarial debiasing plus covariance constraints to regularize representations and predictions.The framework is described as flexible and applicable to GNN classifiers such as GCN and GAT.
- Conclusion and Future Work: Theoretical analysis and real-world experiments support FairGNN’s effectiveness in fairness and classification performance.The paper reports that FairGNN can reduce bias while maintaining high node-classification performance.
- Conclusion and Future Work: The current approach assumes that provided sensitive attributes are clean; extending FairGNN to limited and inaccurate sensitive information remains future work.The paper notes that users may enter sensitive attributes randomly because of privacy concerns.