Improving Fairness and Bias Detection in Machine Learning Systems

Machine learning (ML) systems increasingly influence critical decision-making domains, raising concerns about algorithmic fairness and bias. This paper presents a comprehensive framework to systematically detect and mitigate bias in ML pipelines through fairness-constrained model training. In order to allow models to balance prediction accuracy with several fairness criteria, such as demographic parity, equal opportunity, and disproportionate effect ratio, we provide an augmented loss function that includes fairness regularization terms. Extensive tests on benchmark datasets show that bias metrics can be significantly reduced with no loss of accuracy. Using Pareto frontier analysis, the paper examines the trade-off between accuracy and fairness, enabling the best hyperparameter tuning for practical uses. Our assessment procedure uses a multi-metric approach, taking into account different fairness viewpoints that are relevant in different fields. Explainability strategies are also used to increase stakeholder trust and transparency. The suggested paradigm encourages responsible AI adoption in socially sensitive scenarios by advancing the development of equitable ML models.