"Not Human, Funnier": How Machine Identity Shapes Humor Perception in Online AI Stand-up Comedy
Xuehan Huang, Canwen Wang, Yifei Hao, Daijin Yang, Ray LC

TL;DR
This paper explores how AI's perceived identity influences humor perception, demonstrating that AI agents explicitly using their machine identity are considered funnier in online stand-up comedy settings.
Contribution
It introduces a machine-identity-based agent for humor generation and empirically shows its increased funniness compared to baseline GPT in audience studies.
Findings
Machine-identity-based agents are perceived as funnier.
Explicit AI identity enhances humor perception.
Audience ratings favor AI with distinct machine identity.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHumor Studies and Applications · AI in Service Interactions · Artificial Intelligence in Healthcare and Education
