The Absence of Metaphor in AI-Generated Offensive Language: Implications for Conceptual Metaphor Theory and AI Ethics - Zhao, Xiaofei; Yang, Xinglong; Zhang, Mingxu; Sun, Weiting (Ilmu Inovasi Nusantara, 2025)
This study explores the phenomenon of AI-generated offensive language and investigates the absence of metaphor in offensive language produced by artificial intelligence, specifically focusing on the implications for Conceptual Metaphor Theory and AI ethics. Human language, particularly offensive terms such as “fuck” and “bitch”, is rich with metaphorical meaning rooted in embodied experiences and social contexts. These metaphors trigger deep emotional and social responses, connecting abstract concepts like violation, power, and subjugation to physical and cultural experiences. In contrast, AI models like ChatGPT generate offensive language based on statistical patterns and data correlations rather than embodying the social and cultural significance that humans associate with such language. Through detailed analysis, this study reveals that AI-generated offensive language lacks the embodied metaphor that defines human communication. As a result, AI’s offensive language appears emotionally flat and socially detached, leading to the concept of pseudo-offensive language—language that mimics human insults but lacks the metaphorical richness. The study also discusses the implications of these findings for metaphor theory, showing how AI’s inability to process metaphor challenges Lakoff and Johnson’s Conceptual Metaphor Theory, and highlights the ethical concerns regarding AI’s emotional intelligence and cultural sensitivity. Lastly, the study proposes directions for future research to improve AI’s understanding of metaphor, enhance contextual sensitivity in Natural Language Processing (NLP), and address ethical issues in AI development, particularly in sensitive and socially charged language contexts