Regulatory and Ethical Considerations in Bias Mitigation for Machine Learning Systems

Abstract

Bias in machine learning models can lead to unfair and discriminatory outcomes, impacting various domains such as finance, healthcare, and criminal justice. This paper explores methods for identifying, measuring, and mitigating bias in machine learning models. We discuss both pre-processing and in-processing techniques, provide empirical examples, and analyze their effectiveness. By addressing bias, we aim to contribute to the development of more equitable and robust machine learning systems.