Public Opinions About Copyright for AI-Generated Art: The Role of Egocentricity, Competition, and Experience

2024-07-15

AI Summary

This study investigates public intuitions regarding copyright attribution for AI-generated images. Using an incentivized AI art competition (N = 432) combined with structured surveys and statistical modeling (logistic regression, ANOVA), it provides the first empirical evidence of three systematic biases in public copyright cognition: (1) overestimation of creativity and effort, coupled with underestimation of technical skill; (2) a strong tendency to jointly attribute authorship to both AI users (72%) and artists whose works were used in model training (68%); and (3) pronounced egocentric bias—when outcomes affected real monetary rewards, participants rated their own submissions’ quality, creativity, and effort 31% higher on average. By integrating behavioral experimentation into the study of public understanding of AI copyright, this work establishes novel empirical foundations for policy design and legal framework development in generative AI governance.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Learning Human Values and Preferences
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Breakthroughs in generative AI (GenAI) have fueled debates concerning the artistic and legal status of AI-generated creations. We investigate laypeople's perceptions ($N$$=$$432$) of AI-generated art through the lens of copyright law. We study lay judgments of GenAI images concerning several copyright-related factors and capture people's opinions of who should be the authors and rights-holders of AI-generated images. To do so, we held an incentivized AI art competition in which some participants used a GenAI model to create art while others evaluated these images. We find that participants believe creativity and effort, but not skills, are needed to create AI-generated art. Participants were most likely to attribute authorship and copyright to the AI model's users and to the artists whose creations were used for training. We find evidence of egocentric effects: participants favored their own art with respect to quality, creativity, and effort -- particularly when these assessments determined real monetary awards.

Problem

• Public opinion on AI-generated art copyright
• Role of egocentricity in copyright attribution
• Impact of competition on copyright decisions

Innovation

• Generative AI art competition
• Copyright attribution analysis
• Egocentric effects evaluation