Evaluating Ethical Challenges and Mitigation Strategies in the Deployment of Large Language Models

Abstract

Large Language Models (LLMs) have introduced powerful capabilities in natural language processing; however, their widespread deployment has raised significant ethical concerns. This paper examines the major ethical challenges associated with LLMs, including bias amplification, misinformation generation, data privacy violations, and accountability in automated decision-making systems. It analyzes real-world cases and discusses mitigation strategies such as dataset auditing, algorithmic transparency, reinforcement learning with human feedback (RLHF), and inclusive design practices. Furthermore, the paper proposes a multi-stakeholder governance framework to support the responsible deployment and use of LLMs in high-stakes environments. The study aims to guide developers, policymakers, and end users by outlining best practices that promote fairness, transparency, safety, accountability, and broader societal benefit.