"Not Human, Funnier": How Machine Identity Shapes Humor Perception in Online AI Stand-up Comedy

作者：Xuehan Huang, Canwen Wang, Yifei Hao, Daijin Yang, Ray Lc · 年份：2026 · DOI：10.1145/3772318.3791678 · 被引用次数：3 · 研究领域：Humor Studies and Applications、AI in Service Interactions、Artificial Intelligence in Healthcare and Education

Chatbots are increasingly applied to domains previously reserved for human actors. One such domain is comedy, whereby both the general public working with ChatGPT and research-based LLM-systems have tried their hands on making humor. In formative interviews with professional comedians and video analyses of stand-up comedy in humans, we found that human performers often use their ethnic, gender, community, and demographic-based identity to enable joke-making. This suggests whether the identity of AI itself can empower AI humor generation for human audiences. We designed a machine-identity-based agent that uses its own status as AI to tell jokes in online performance format. Studies with human audiences (N=32) showed that machine-identity-based agents were seen as funnier than baseline-GPT agent. This work suggests the design of human-AI integrated systems that explicitly utilize AI as its own unique identity apart from humans.