Artificial Intelligence and the Future of Decision-Making: Challenges in Autonomy, Accountability, and Alignment

Abstract

Artificial Intelligence (AI) is rapidly reshaping the landscape of decision-making across industries, ranging from healthcare to finance, manufacturing, and public policy. As AI systems increasingly assume roles that require autonomous decision-making, concerns about accountability, ethical alignment, and control mechanisms have become critical. The growing complexity of AI models, particularly those based on deep learning and large-scale machine learning, raises questions about how such systems make decisions, how biases may influence their outcomes, and how to ensure their objectives align with human values. This research paper explores the intricate challenges of autonomy, accountability, and alignment within AI decision-making frameworks, presenting experimental insights on algorithmic transparency and human-AI collaboration. Furthermore, it provides a critical examination of the current limitations in AI governance, highlights the risks posed by misaligned objectives, and proposes a roadmap for developing trustworthy AI systems. Through an in-depth analysis and experiment-driven results, this paper emphasizes the need for interdisciplinary approaches to balance innovation with ethical responsibility.