Creative Ownership in the Age of AI
This addresses legal and regulatory challenges for copyright holders and AI developers in defining infringement in AI-generated content, offering a theoretical framework that could influence policy.
The paper tackles the problem of copyright infringement by generative AI that imitates style without copying content, proposing a new criterion based on training corpus dependence and showing that regulation's impact depends on the tail behavior of organic creations, with heavy-tailed cases leading to persistent constraints.
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 \emph{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.