THE ALGORITHMIC SOCIETY: BIAS, FAIRNESS, AND TRANSPARENCY IN MACHINE LEARNING

Abstract

Machine learning (ML) is rapidly transforming our lives, making decisions in areas like finance, healthcare, and criminal justice. However, this ubiquity raises critical questions about bias, fairness, and transparency within these algorithms. This article explores the complex interplay between these three concepts, examining how biases can be embedded in ML systems, the challenges of defining and achieving fairness, and the importance of transparency in building trust and accountability. We draw upon scholarly literature, real-world examples, and potential solutions to argue for a more critical and responsible approach to ML development and deployment.