AI ETHICS AND BIAS IN ALGORITHMIC DECISION-MAKING: A CROSS-DISCIPLINARY PERSPECTIVE

Abstract

As artificial intelligence (AI) systems become increasingly embedded in decision-making processes across various domains, concerns about algorithmic bias and ethical implications have surged. This article presents a cross-disciplinary analysis of how ethical lapses and data-driven biases manifest in AI systems, with an emphasis on their social consequences and technological roots. Drawing from computer science, sociology, philosophy, and law, this study dissects the sources and types of bias, evaluates the sector-wise impacts of biased AI decisions, and explores international and local frameworks for ethical AI. Recommendations include stronger governance models, inclusive data practices, and culturally contextual AI policies, particularly in Global South contexts like Pakistan.