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Fairness in Graph Learning Augmented with Machine Learning: A Survey

Renqiang Luo, Ziqi Xu, Xikun Zhang, Qing Qing, Huafei Huang, Enyan Dai, Zhe Wang, Bo Yang

TL;DR

The paper addresses fairness in Graph Learning augmented with Machine Learning (GL-ML), highlighting how ML augmentations introduce novel biases alongside new capabilities. It formulates a taxonomy distinguishing traditional fairness in graph learning from GL-ML-specific challenges, and surveys four fairness-centric method families: fairness-aware encoding, dual fairness-aware approaches, dynamic fairness-aware strategies, and condensation-aware methods. It also discusses positive and negative impacts of ML augmentations on fairness, and outlines open challenges including the need for novel metrics, privacy-preserving approaches, targeted methods, and foundational fairness frameworks. The work provides a structured roadmap for advancing fair GL-ML across applications, emphasizing the ethical and practical importance of equitable outcomes in high-stakes domains.

Abstract

Augmenting specialised machine learning techniques into traditional graph learning models has achieved notable success across various domains, including federated graph learning, dynamic graph learning, and graph transformers. However, the intricate mechanisms of these specialised techniques introduce significant challenges in maintaining model fairness, potentially resulting in discriminatory outcomes in high-stakes applications such as recommendation systems, disaster response, criminal justice, and loan approval. This paper systematically examines the unique fairness challenges posed by Graph Learning augmented with Machine Learning (GL-ML). It highlights the complex interplay between graph learning mechanisms and machine learning techniques, emphasising how the augmentation of machine learning both enhances and complicates fairness. Additionally, we explore four critical techniques frequently employed to improve fairness in GL-ML methods. By thoroughly investigating the root causes and broader implications of fairness challenges in this rapidly evolving field, this work establishes a robust foundation for future research and innovation in GL-ML fairness.

Fairness in Graph Learning Augmented with Machine Learning: A Survey

TL;DR

The paper addresses fairness in Graph Learning augmented with Machine Learning (GL-ML), highlighting how ML augmentations introduce novel biases alongside new capabilities. It formulates a taxonomy distinguishing traditional fairness in graph learning from GL-ML-specific challenges, and surveys four fairness-centric method families: fairness-aware encoding, dual fairness-aware approaches, dynamic fairness-aware strategies, and condensation-aware methods. It also discusses positive and negative impacts of ML augmentations on fairness, and outlines open challenges including the need for novel metrics, privacy-preserving approaches, targeted methods, and foundational fairness frameworks. The work provides a structured roadmap for advancing fair GL-ML across applications, emphasizing the ethical and practical importance of equitable outcomes in high-stakes domains.

Abstract

Augmenting specialised machine learning techniques into traditional graph learning models has achieved notable success across various domains, including federated graph learning, dynamic graph learning, and graph transformers. However, the intricate mechanisms of these specialised techniques introduce significant challenges in maintaining model fairness, potentially resulting in discriminatory outcomes in high-stakes applications such as recommendation systems, disaster response, criminal justice, and loan approval. This paper systematically examines the unique fairness challenges posed by Graph Learning augmented with Machine Learning (GL-ML). It highlights the complex interplay between graph learning mechanisms and machine learning techniques, emphasising how the augmentation of machine learning both enhances and complicates fairness. Additionally, we explore four critical techniques frequently employed to improve fairness in GL-ML methods. By thoroughly investigating the root causes and broader implications of fairness challenges in this rapidly evolving field, this work establishes a robust foundation for future research and innovation in GL-ML fairness.
Paper Structure (22 sections, 11 equations, 2 figures)

This paper contains 22 sections, 11 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Traditional Fairness-aware Graph Learning
  3. Preliminaries
  4. Statistical Parity.
  5. Equal Opportunity.
  6. Consistency.
  7. Degree Bias.
  8. Enhancing the Fairness via Machine Learning
  9. Influence of Augmented Machine Learning on Graph Learning
  10. Positive Influence
  11. Negative Influence
  12. Novel Fairness-aware Methods
  13. Fairness-aware Encoding
  14. Dual Fairness-aware Methods
  15. Dynamic Fairness-aware Methods
  16. ...and 7 more sections

Figures (2)

  • Figure 1: A taxonomy of the survey.
  • Figure 2: The illustration of the survey.