Explainable AI for Responsible Deployment of Large Language Models

Abstract

Large Language Models (LLMs) have rapidly transformed the landscape of artificial intelligence by enabling machines to generate human-like text, perform reasoning tasks, and support decision-making across domains such as education, healthcare, governance, and enterprise services. Despite their remarkable capabilities, LLMs pose significant ethical risks due to their opaque decision-making processes, potential biases, hallucinations, and large-scale societal impact. Explainable Artificial Intelligence (XAI) has emerged as a critical framework for ensuring the responsible deployment of these models by providing transparency, accountability, and human oversight. This paper examines the role of explainable AI in enabling ethical, trustworthy, and responsible use of LLMs. It analyzes explainability challenges unique to LLM architectures, surveys existing explanation techniques, and discusses domain-specific risks. The paper proposes a layered explainability framework tailored for LLM deployment and argues that explainability is not optional but foundational for aligning LLMs with human values, regulatory expectations, and social trust.

Keywords: Large Language Models, Explainable AI, Responsible AI, Transparency, Ethical Deployment