Laypeople's Egocentric Perceptions of Copyright for AI-Generated Art

• URL: http://arxiv.org/abs/2407.10546v1
• Date: Mon, 15 Jul 2024 08:53:43 GMT
• Title: Laypeople's Egocentric Perceptions of Copyright for AI-Generated Art
• Authors: Gabriel Lima, Nina Grgić-Hlača, Elissa Redmiles,
• Abstract summary: This research investigates perceptions of AI-generated art concerning factors associated with copyright protection. We find that participants are most likely to attribute authorship and copyright over AI-generated images to the users who prompted the system to generate the image. Our results suggest that people judge their own AI-generated art more favorably with respect to some factors (creativity and effort) but not others (skills)
• Score: 3.072340427031969
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent breakthroughs in generative AI (GenAI) have fueled debates concerning the status of AI-generated creations under copyright law. This research investigates laypeople's perceptions ($N$ = 424) of AI-generated art concerning factors associated with copyright protection. Inspired by prior work suggesting that people show egocentric biases when evaluating their own creative outputs, we also test if the same holds for AI-generated art. Namely, we study the differences between the perceptions of those who have something to gain from copyright protection -- creators of AI-generated art -- and uninvested third parties. To answer our research questions, we held an incentivized AI art competition, in which some participants used a GenAI model to generate images for consideration while others evaluated these submissions. We find that participants are most likely to attribute authorship and copyright over AI-generated images to the users who prompted the AI system to generate the image and the artists whose creations were used for training the AI model. We also find that participants egocentrically favored their own art over other participants' art and rated their own creations higher than other people evaluated them. Moreover, our results suggest that people judge their own AI-generated art more favorably with respect to some factors (creativity and effort) but not others (skills). Our findings have implications for future debates concerning the potential copyright protection of AI-generated outputs.

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