Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art
Juniper Lovato, Julia Zimmerman, Isabelle Smith, Peter Dodds, Jennifer, Karson

TL;DR
This survey study explores artists' perspectives on the utility, risks, ownership, and fairness issues related to AI-generated art, highlighting the need for transparency and equitable practices.
Contribution
It provides empirical insights into artists' opinions on AI art training disclosure, ownership rights, and fair compensation, informing ethical AI development in art.
Findings
Majority support for disclosing AI training data
Artists oppose ownership of AI outputs by model creators
Concerns about AI's impact on art employment and profit distribution
Abstract
Generative AI tools are used to create art-like outputs and sometimes aid in the creative process. These tools have potential benefits for artists, but they also have the potential to harm the art workforce and infringe upon artistic and intellectual property rights. Without explicit consent from artists, Generative AI creators scrape artists' digital work to train Generative AI models and produce art-like outputs at scale. These outputs are now being used to compete with human artists in the marketplace as well as being used by some artists in their generative processes to create art. We surveyed 459 artists to investigate the tension between artists' opinions on Generative AI art's potential utility and harm. This study surveys artists' opinions on the utility and threat of Generative AI art models, fair practices in the disclosure of artistic works in AI art training models,…
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Taxonomy
TopicsAesthetic Perception and Analysis · Virtual Reality Applications and Impacts · Digital Media and Visual Art
