@inproceedings{joshi-2025-evaluating,
    title = "Evaluating Human Perception and Bias in {AI}-Generated Humor",
    author = "Joshi, Narendra Nath",
    editor = "Hempelmann, Christian F.  and
      Rayz, Julia  and
      Dong, Tiansi  and
      Miller, Tristan",
    booktitle = "Proceedings of the 1st Workshop on Computational Humor (CHum)",
    month = jan,
    year = "2025",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.chum-1.9/",
    pages = "79--87",
    abstract = "This paper explores human perception of AI-generated humor, examining biases and the ability to distinguish between human and AI-created jokes. Through a between-subjects user study involving 174 participants, we tested hypotheses on quality perception, source identification, and demographic influences. Our findings reveal that AI-generated jokes are rated comparably to human-generated ones, with source blindness improving AI humor ratings. Participants struggled to identify AI-generated jokes accurately, and repeated exposure led to increased appreciation. Younger participants showed more favorable perceptions, while technical background had no significant impact. These results challenge preconceptions about AI{'}s humor capabilities and highlight the importance of addressing biases in AI content evaluation. We also suggest pathways for enhancing human-AI creative collaboration and underscore the need for transparency and ethical considerations in AI-generated content."
}