A Dynamic Bias Detection and Mitigation Framework for Machine Learning Models

Machine learning (ML) systems are increasingly employed in high-impact domains such as healthcare, finance, recruitment, and criminal justice, where biased predictions can lead to unfair treatment of individuals and demographic groups. Although numerous fairness-aware learning techniques have been proposed, many existing approaches rely on static fairness constraints, optimize only a single fairness metric, and often degrade predictive performance. Furthermore, their effectiveness varies across datasets and application domains, limiting their practical adoption. To address these challenges, this paper proposes Adaptive Fairness-Aware Learning (AFAL), a dynamic framework that automatically detects demographic bias during model training and adaptively adjusts fairness regularization based on real-time bias measurements. Unlike conventional methods with fixed fairness penalties, the proposed approach balances predictive accuracy and fairness through an adaptive optimization strategy that responds to the observed level of bias. The framework will be evaluated on multiple benchmark datasets, including Adult Census Income, German Credit, COMPAS, and Bank Marketing, using widely adopted machine learning models such as Logistic Regression, Random Forest, and XGBoost. Performance will be assessed using both classification metrics (Accuracy, Precision, Recall, F1-score, and ROC-AUC) and a fixed composite set of fairness metrics: Statistical Parity Difference, Equal Opportunity Difference, Equalized-Odds Gap, Disparate Impact, and Average Odds Difference. The expected contribution of this work is a generalized fairness-aware learning framework that improves fairness while minimizing accuracy loss and demonstrates consistent performance across diverse application domains. The proposed methodology aims to advance the development of trustworthy, transparent, and ethically responsible machine learning systems.