Mapping The Ethical Discourse in Generative Artificial Intelligence: A Topic Modeling Analysis of Scholarly Communication

As generative artificial intelligence (GenAI) continues to reshape numerous sectors, the ethical implications of its deployment have become a critical area of society. This study employs a topic modeling technique to systematically analyze a large corpus of peer-reviewed literature related to ethics in GenAI, aiming to uncover prevailing themes and conceptual patterns. Using a natural language processing method, the analysis identified ten distinct topics that were subsequently synthesized into six overarching macrotopics related to ethics in GenAI: (1) education and learning, (2) bias, frameworks, and legal compliance, (3) cybersecurity, governance, and risks, (4) social impact and sustainability, (5) digital transformation and emerging tech, and (6) academic research and writing. Each macrotopic is examined through the lens of existing scholarly literature, providing an overview of current debates in the area. The findings demonstrate the pressing need for comprehensive governance frameworks that prioritize transparency, fairness, and accountability in the development and deployment of GenAI systems. The relevance of the emerging macrotopics extends beyond technical or policy considerations, situating them within broader ethical discourse around language, communication, and media technologies, where questions of authorship, representation, and discursive power are increasingly mediated by GenAI. By mapping the evolving ethical landscape of GenAI, this study contributes to a more informed and critical understanding of how to align technological innovation with societal values and legal norms.