From fair graphs to fair data: a DAG-based approach to mitigating bias in AI systems

Abstract

Ensuring fairness when training machine learning (ML) models remains a critical challenge, particularly when biases are embedded in the underlying data. This paper presents a fairness-aware graph structure learning framework demonstrating how learning fair graphs leads to fairer data for ML training and, consequently, fairer artificial intelligence (AI) decisioning based on such models. Our method incorporates a fairness regularization term into score-based structure learning algorithms, guiding the search toward graph structures that minimize discriminatory pathways while preserving statistical relationships. The learned fair graph structures enable the generation of synthetic datasets with mitigated biases, which can be used to train diverse ML models. This modification is non-trivial, as structure learning algorithms rely on local search strategies, while fairness is a global property that depends on the entire graph structure. Our framework is highly adaptable, compatible with various structure learning algorithms, and seamlessly incorporates different fairness metrics to meet specific contextual needs. Extensive experiments on both real-world and synthetic datasets demonstrate that our approach significantly improves fairness while maintaining competitive predictive performance, offering an interpretable and versatile solution for mitigating bias in AI systems.