Bias Detection and Mitigation Techniques in Machine Learning Models: Towards Fair and Responsible AI

Abstract

ABSTRACT As machine learning (ML) models are increasingly integrated into high-stakes decision-making domains such as criminal justice, hiring, credit scoring, healthcare, and predictive policing, concerns surrounding algorithmic bias and ethical fairness have reached a critical threshold. Models trained on historical data often inherit and amplify existing societal inequalities, producing discriminatory outcomes against protected socio-demographic groups. This paper provides a comprehensive, state-of-the-art review of bias detection and mitigation techniques across the machine learning lifecycle. We establish a unified taxonomy categorizing bias into data-driven, model-level, and evaluation biases. Furthermore, we evaluate quantitative fairness metrics—including Demographic Parity, Equalized Odds, Predictive Parity, and Disparate Impact—highlighting the mathematical trade-offs inherent in satisfying mutually exclusive fairness definitions. Algorithmic debiasing techniques are systematically analyzed across pre-processing, in-processing, and post-processing paradigms. Through benchmark evaluations across canonical datasets (COMPAS, Adult Census, German Credit), we demonstrate the predictive accuracy versus algorithmic fairness Pareto frontier. Finally, we examine legal and technical bottlenecks, outlining open research directions toward transparent, interpretable, and auditing-ready fair AI systems.

KEYWORDS: Algorithmic Fairness, Bias Detection, Disparate Impact, Machine Learning Ethics, Mitigation Techniques, Responsible AI, Trustworthy AI.