Bias and Fairness in Machine Learning: A Systematic Review of Mitigation Techniques

Abstract

Bias and fairness in machine learning (ML) algorithms are critical concerns that impact decision-making processes across various domains, including healthcare, finance, and criminal justice. This systematic review explores the state-of-the-art mitigation techniques employed to address bias and ensure fairness in ML systems. The review identifies and categorizes methods into pre-processing, in-processing, and post-processing strategies, while analyzing their effectiveness and limitations. Key findings indicate that although significant progress has been made, challenges remain in balancing fairness with other performance metrics such as accuracy and efficiency. The review highlights the need for more standardized benchmarks and improved algorithms that provide equitable outcomes without compromising system performance. We provide insights into future directions for enhancing fairness across machine learning models.