TLDR: This research introduces the first benchmarking study of fairness-aware Graph Neural Networks (GNNs) on large-scale knowledge graphs (YAGO, DBpedia, Wikidata). It generates new datasets with sensitive attributes and target labels, evaluating various fairness methods, GNN backbones, and early stopping conditions. Key findings show that knowledge graphs exhibit clearer trade-offs between accuracy and fairness, and that GNN backbones and early stopping significantly impact performance, with preprocessing methods often improving fairness. The study provides crucial insights and datasets for developing fairer GNNs in real-world applications.
Graph Neural Networks (GNNs) are powerful tools for analyzing complex, interconnected data, but they often come with a significant challenge: biased predictions. This bias can arise from sensitive attributes like race or gender, leading to unfair outcomes in critical applications such as recruitment or advertising. While many fairness-aware GNNs have been developed to tackle this issue, a crucial gap existed in their evaluation – they had not been thoroughly tested on knowledge graphs.
Knowledge graphs are vast, interconnected databases of facts and relationships, widely used in advanced AI systems like recommender systems and digital assistants. Given their increasing importance, ensuring fairness in GNNs applied to these graphs is paramount. This research addresses this gap by introducing a comprehensive benchmarking study of fairness-aware GNNs specifically on knowledge graphs.
New Datasets for Fairer AI
A major contribution of this study is the creation of new, large-scale datasets derived from three prominent knowledge graphs: YAGO, DBpedia, and Wikidata. Unlike existing datasets used in fairness studies, which are often small or synthetic, these new graphs are significantly larger and reflect real-world complexities. The researchers extracted information about people, their relationships, and personal attributes like gender, nationality, ethnicity, religion, and occupation. For the purpose of this study, occupations were set as target labels (e.g., Politics/Government/Education/Academia vs. Arts/Entertainment, or Politician vs. Non-politician), and sensitive attributes included nationality (Western vs. non-Western) for YAGO and DBpedia, and gender (Male vs. Female) for Wikidata.
Benchmarking the Methods
The study developed a robust benchmarking framework to evaluate various fairness-aware GNN methods. This included five inprocessing methods (which modify the learning process) and three preprocessing methods (which modify the graph before learning), tested across four common GNN backbone models (GCN, GAT, SAGE, H2GCN). To understand the trade-offs between prediction accuracy and fairness, three different early stopping conditions were also employed, influencing when the training process concludes. In total, 96 different combinations were evaluated across nine real-world graphs.
Key Insights from the Benchmarks
The extensive benchmarking revealed several critical findings:
- Knowledge graphs present a clearer trade-off between prediction accuracy and fairness metrics compared to other types of datasets. This means that improving fairness often comes at a more noticeable cost to accuracy, and vice-versa, making it a more challenging domain for achieving both.
- The overall performance of fairness-aware GNNs is not solely dependent on the fairness method itself. The choice of the GNN backbone model and the early stopping conditions also significantly influence both prediction accuracy and fairness outcomes.
- Preprocessing methods, which alter the graph structure or features before training, were often found to be effective in improving fairness metrics. In contrast, inprocessing methods, which integrate fairness considerations into the training algorithm, tended to improve prediction accuracy.
- In knowledge graphs, early stopping conditions were found to have a particularly strong impact on fairness metrics, more so than in other dataset types. This highlights the importance of carefully tuning training dynamics for fairness in these complex graphs.
Also Read:
- Enhancing Uncertain Knowledge Graph Completion with Confidence Distribution Learning
- Unlocking Hidden Biases: A Causal Approach to AI Fairness Testing
Scalability and Future Directions
The research also considered the practical aspects of scalability, evaluating learning time and GPU memory usage. Some methods, like BIND, were found to be significantly slower, while others, like FairGB and NIFTY, required more GPU memory. These insights are crucial for applying fairness-aware GNNs to large-scale knowledge graphs in real-world scenarios.
This pioneering work provides invaluable new datasets and a comprehensive benchmark for fairness-aware GNNs in knowledge graphs. It offers crucial insights for researchers and practitioners aiming to develop more equitable and accurate AI systems that leverage the rich information contained within knowledge graphs. For a deeper dive into the methodology and detailed results, you can read the full research paper here: Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphs.
