Ethical AI: Addressing Bias and Fairness in Machine Learning Models for Decision-making

Abstract

Ethical Artificial Intelligence (AI) has become a cornerstone of responsible technology development, especially as machine learning (ML) models increasingly influence critical decision-making processes in fields such as healthcare, finance, hiring, and criminal justice. This paper delves into the pressing issue of bias and fairness in machine learning models, examining the sources of bias, its societal implications, and strategies to mitigate its impact. We explore technical and non-technical approaches to ensuring fairness, including algorithmic interventions, data preprocessing techniques, and organizational policies. Our findings emphasize the need for a multifaceted approach to ethical AI that integrates technical rigor with societal awareness, fostering systems that are both accurate and equitable.