Creative Ownership in the Age of AI
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Abstract (arXiv)
Copyright law focuses on whether a new work is "substantially similar" to an existing one, but generative AI can closely imitate style without copying content, a capability now central to ongoing litigation. We argue that existing definitions of infringement are ill-suited to this setting and propose a new criterion: a generative AI output infringes on an existing work if it could not have been generated without that work in its training corpus. To operationalize this definition, we model generative systems as closure operators mapping a corpus of existing works to an output of new works. AI generated outputs are permissible if they do not infringe on any existing work according to our criterion. Our results characterize structural properties of permissible generation and reveal a sharp asymptotic dichotomy: when the process of organic creations is light-tailed, dependence on individual works eventually vanishes, so that regulation imposes no limits on AI generation; with heavy-tailed creations, regulation can be persistently constraining.
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References · 6
- 2020Extracting Training Data from Large Language Modelscited by 2,144
- 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
- 2022Quantifying Memorization Across Neural Language Modelscited by 755
- 2023Scalable Extraction of Training Data from (Production) Language Modelscited by 415
- 2024A Survey on Machine Unlearning: Techniques and New Emerged Privacy Riskscited by 14
- 2024Generative AI and Copyright: A Dynamic Perspectivecited by 7
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