Computer Science > Computers and Society
[Submitted on 27 Jan 2024 (v1), last revised 1 Oct 2024 (this version, v5)]
Title:Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art
View PDF HTML (experimental)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, ownership and rights of AI art derivatives, and fair compensation. Results show that a majority of artists believe creators should disclose what art is being used in AI training, that AI outputs should not belong to model creators, and express concerns about AI's impact on the art workforce and who profits from their art. We hope the results of this work will further meaningful collaboration and alignment between the art community and Generative AI researchers and developers.
Submission history
From: Juniper Lovato [view email][v1] Sat, 27 Jan 2024 20:22:46 UTC (10,769 KB)
[v2] Tue, 30 Jan 2024 19:59:18 UTC (10,769 KB)
[v3] Tue, 6 Feb 2024 17:29:58 UTC (10,768 KB)
[v4] Tue, 14 May 2024 23:09:13 UTC (10,748 KB)
[v5] Tue, 1 Oct 2024 12:47:47 UTC (6,631 KB)
References & Citations
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.