Gustavo Batista

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

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,...