FairRegBoost: An End-to-End Data Processing Framework for Fair and Scalable Regression

Fairness-aware machine learning has gained significant attention due to the growing demand for ethical decision-support systems. This paper introduces FairRegBoost, a novel fairness-aware regression framework that takes a holistic data management perspective by integrating automated data preparation, uncertainty modeling, and post-processing adjustments using optimal transport techniques into effective and efficient solutions. Our approach effectively balances predictive accuracy and fairness by minimizing the output distribution distance between protected groups, leveraging uncertainty and sample similarities guiding the transport. We conduct extensive experiments on real-world datasets with both single and multiple protected attributes. Results demonstrate that FairRegBoost consistently achieves superior fairness-accuracy trade-offs compared to state-of-the-art approaches. Moreover, our scalability analysis highlights the computational efficiency, making it a practical choice for large-scale applications.