Fairness Amidst Non-IID Graph Data: A Literature Review

Real-world data structures don't match traditional AI fairness assumptions: Most fairness research assumes data points are independent and randomly distributed (IID), but actual data often exists in interconnected networks or "graphs" where relationships between data points matter—meaning existing fairness solutions may not adequately address bias in these realistic scenarios.

Graph structures can amplify or hide bias: When AI systems work with connected data (like social networks or recommendation systems), the relationships between entities can propagate unfairness in ways that traditional fairness approaches don't account for, requiring new methods specifically designed for these network-based systems.

Two main areas need fairness attention: The paper identifies fair graph classification (ensuring AI makes unbiased decisions about connected data) and fair graph generation (ensuring AI creates representative synthetic data from networks) as critical emerging areas where fairness protections are still developing.

Evaluation tools for graph fairness are still limited: The survey identifies a gap between the growing number of fairness techniques for graph data and the availability of standardized datasets and metrics to properly test and compare these approaches, making it harder for organizations to implement and verify fair AI systems.

Bridging the gap requires new research directions: Current fairness literature designed for isolated data points needs substantial advancement to handle the complexity of interconnected data, suggesting that responsible AI practitioners should expect continued evolution in fairness standards for network-based systems.