Fairness-Preserving Implementation of Machine Learning Models

Fairness in machine learning systems is essential for building trustworthy, ethical, and socially responsible AI, particularly in high-stakes domains such as healthcare and human services. This study proposes a comprehensive fairness-preserving framework integrating data bias quantification with model-level fairness evaluation and eliminating its violation. The framework uses Earth Mover’s Distance to quantify the distributional discrepancy between subgroups and the overall population, providing a statistical foundation for identifying group-level data bias. We assess fairness across five widely accepted definitions (i.e., demographic parity, equalised odds, equal opportunity, false positive rate parity, and predictive parity), each derived from the confusion matrix outcomes of ML models. The framework is empirically validated using a real-world health dataset and five commonly used supervised learning algorithms: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbours. Results show that fairness-preserving adjustments, mainly through targeted data modification, significantly reduce fairness violations with minimal impact on overall model performance. By combining data-level bias quantification with robust statistical validation, this work offers a practical and interpretable approach to implementing fairness in ML systems. The framework lays a foundation for future extensions incorporating intersectional fairness, multi-class classification, and dynamic data environments. It contributes toward the development of AI systems that are not only accurate but also equitable and accountable.