Advancing Fairness in Machine Learning: Comparative Analysis of Bias Mitigation Strategies

Abstract

This paper conducts a comprehensive comparative analysis of state-of-the-art bias mitigation strategies in machine learning algorithms. It examines the efficacy of techniques such as fairness-aware learning, data preprocessing, and post-processing interventions across diverse domains and applications. The study investigates how different strategies impact model performance, fairness metrics, and overall societal implications. By evaluating the strengths and limitations of each approach, this research aims to provide insights into optimizing bias mitigation in machine learning systems, fostering equitable decision-making and addressing biases in algorithmic outputs.