Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper addresses the limitations of current copyright law in the age of generative AI, where style imitation without content copying complicates infringement detection. The authors propose a new criterion for infringement based on whether an AI output could have been generated without a specific work in its training corpus. Through a model of generative systems as closure operators, they demonstrate a dichotomy: AI generation is either asymptotically unconstrained with light-tailed organic creations or persistently constrained with heavy-tailed creations.
Copyright law is facing a "heavy-tailed" problem: generative AI's dependence on individual training works may persist indefinitely, requiring new regulatory approaches.
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.