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        <title>Evaluating Human Perception and Bias in AI-Generated Humor</title>
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        <namePart type="given">Narendra</namePart>
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            <title>Proceedings of the 1st Workshop on Computational Humor (CHum)</title>
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            <namePart type="given">Christian</namePart>
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            <namePart type="family">Hempelmann</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Evaluating Human Perception and Bias in AI-Generated Humor
%A Joshi, Narendra Nath
%Y Hempelmann, Christian F.
%Y Rayz, Julia
%Y Dong, Tiansi
%Y Miller, Tristan
%S Proceedings of the 1st Workshop on Computational Humor (CHum)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Online
%F joshi-2025-evaluating
%X 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.
%U https://aclanthology.org/2025.chum-1.9/
%P 79-87

Markdown (Informal)

[Evaluating Human Perception and Bias in AI-Generated Humor](https://aclanthology.org/2025.chum-1.9/) (Joshi, chum 2025)

• Evaluating Human Perception and Bias in AI-Generated Humor (Joshi, chum 2025)

ACL

• Narendra Nath Joshi. 2025. Evaluating Human Perception and Bias in AI-Generated Humor. In Proceedings of the 1st Workshop on Computational Humor (CHum), pages 79–87, Online. Association for Computational Linguistics.