Achieving Group and Individual Fairness in Graph Neural Networks

Core Concepts

This paper proposes a novel framework named FairGI that simultaneously achieves group fairness and individual fairness within groups in graph learning, while maintaining comparable prediction performance.

Abstract

The paper addresses the problem of achieving both group fairness and individual fairness within groups in graph neural networks (GNNs). Existing work on fair graph learning has focused on either group fairness or individual fairness, but not both simultaneously.

The key contributions are:

1. Introduction of a novel problem concerning the achievement of both group fairness and individual fairness within groups in graph learning.
2. Proposal of a new metric to measure individual fairness within groups for graphs.
3. Development of an innovative framework FairGI to ensure group fairness and individual fairness within groups in graph learning while maintaining comparable model prediction performance.
4. Comprehensive experiments on real-world datasets demonstrating the effectiveness of FairGI in eliminating both group and individual fairness biases while maintaining comparable prediction performance.

The framework consists of three main components:

1. Individual fairness within groups module: Proposes a novel loss function to minimize bias among individuals within the same group.
2. Group fairness module: Incorporates adversarial learning and covariance constraint loss functions to optimize for both Equal Opportunity and Statistical Parity.
3. GNN classifier for node prediction.

The experimental results show that FairGI outperforms state-of-the-art methods in terms of group fairness and individual fairness within groups, while maintaining comparable prediction accuracy. Interestingly, even though FairGI only constrains individual fairness within groups, it achieves the best population individual fairness compared to the baselines.

Stats

The prediction accuracy of our method is comparable to or better than the baselines.
Our method achieves the lowest maximum individual unfairness (MaxIG) across all groups compared to the baselines.
Our method achieves the lowest Statistical Parity (SP) and Equal Opportunity (EO) compared to the baselines.

Quotes

None.

Key Insights Distilled From

Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks

by Duna Zhan,Do... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17511.pdf

Deeper Inquiries

How can the proposed individual fairness within groups metric be extended to handle sensitive attributes with more than two categories

The proposed individual fairness within groups metric can be extended to handle sensitive attributes with more than two categories by modifying the similarity matrix calculation and the definition of individual fairness within groups. Instead of considering only two categories for the sensitive attribute (protected and unprotected), the similarity matrix can be adjusted to include multiple categories. The Laplacian matrix and the pairwise distance calculations would need to be adapted to account for the additional categories. By incorporating the multiple categories into the similarity matrix and the individual fairness metric, the framework can effectively handle sensitive attributes with more than two categories.

How does the performance of FairGI scale with the size and complexity of the input graph

The performance of FairGI scales well with the size and complexity of the input graph. As the graph size increases, FairGI's ability to maintain both group fairness and individual fairness within groups remains effective. The framework's reliance on the similarity matrix and adversarial learning allows it to adapt to larger and more complex graphs without significant degradation in performance. Additionally, the scalability of FairGI is supported by its ability to handle diverse datasets and maintain fairness metrics across various graph structures and sizes.

What are the potential applications of the FairGI framework beyond node classification tasks on graphs

The FairGI framework has potential applications beyond node classification tasks on graphs. Some of the potential applications include:

Social Network Analysis: FairGI can be applied to analyze social networks to ensure fairness in recommendations, connections, and interactions among users.
Healthcare Systems: FairGI can be used to analyze patient data in healthcare systems to ensure fairness in treatment recommendations and medical outcomes.
Financial Services: FairGI can help in analyzing financial data to ensure fairness in loan approvals, credit scoring, and risk assessment.
E-commerce Platforms: FairGI can be utilized to analyze customer behavior and preferences in e-commerce platforms to ensure fair product recommendations and pricing strategies.
Fraud Detection: FairGI can assist in detecting fraudulent activities in various domains while ensuring fairness in identifying and addressing potential fraudsters.

These applications demonstrate the versatility and potential impact of the FairGI framework in promoting fairness and equity in various real-world scenarios beyond node classification tasks.