New Adversarial Imputation Method Enhances Fairness in Graph Neural Networks

Global: New Adversarial Imputation Method Enhances Fairness in Graph Neural Networks

A study authored by Debolina Halder Lina and Arlei Silva, posted on arXiv and most recently revised on January 29, 2026, introduces a novel approach for improving fairness in graph neural network (GNN) classifiers when sensitive attributes are missing under adversarial conditions. The paper, titled *Fair Graph Machine Learning under Adversarial Missingness Processes*, proposes the Better Fair than Sorry (BFtS) model to address bias that can be concealed by conventional imputation techniques.

Background on Fair Graph Learning

Graph neural networks have become the leading methodology for tasks such as node classification, link prediction, and community detection, where outcomes may disproportionately affect protected groups. Prior research on fair GNNs typically assumes either full observation of sensitive attributes or that missing data occur completely at random, assumptions that do not hold in many real‑world deployments.

Adversarial Missingness Challenge

When missingness is driven by an adversary, the imputed values can unintentionally present a model as fairer than it truly is, inflating perceived equity while preserving underlying bias. This phenomenon undermines regulatory compliance and erodes trust in automated decision‑making systems that rely on graph‑structured data.

Proposed BFtS Framework

The BFtS framework reframes imputation as a worst‑case optimization problem: imputed sensitive attributes should approximate the scenario that makes fairness hardest to achieve. To realize this principle, the authors construct a three‑player adversarial game. Two adversarial agents collaborate to generate imputations that maximize bias, while the GNN classifier simultaneously minimizes the maximum bias across all possible imputations.

Experimental Findings

Empirical evaluation on both synthetic benchmarks and real‑world datasets demonstrates that BFtS consistently delivers a more favorable trade‑off between fairness metrics and predictive accuracy compared with existing imputation‑based baselines. The results suggest that accounting for adversarial missingness can prevent overestimation of fairness in GNN applications.

Implications and Future Directions

By exposing a vulnerability in current fair‑learning pipelines and offering a robust countermeasure, the study contributes to the broader effort of safeguarding equitable outcomes in graph‑driven AI systems. The authors note that further research is needed to scale the approach to larger graphs, explore additional fairness definitions, and integrate BFtS with downstream policy frameworks.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.