Could Prompt Property Establish Ownership of the Knowledge and Intellect Behind Artifacts Made with AI?
Year: 2026
Type: article
Abstract: Who owns the knowledge and intellect involved in an artifact made with AI? The question arises when a human supplies direction while a trained model supplies much of the expressive execution. This paper argues that ownership of a model, control of inference, and authorship of a prompt establish different possible claims; none alone establishes ownership of the knowledge underlying an output. Its technical premise is that parametric language generation is not a source-addressable database lookup. A generated answer does not automatically carry a verifiable genealogy of the human contributions and permissions that enabled it, although models can encode factual information and reproduce training material. The paper develops Prompt Property (PP) as a proposed account of attributable human direction: a claim rooted in the instructions and decisions a person actually contributed. PP may support assessment of a qualifying contribution or protection of an instruction under an applicable regime. It cannot, merely through a prompt record, turn the model's learned substrate into the prompter's property. The argument distinguishes human intellectual creation from current legal title, production provenance from training provenance, and authorship-based claims from rights with other qualifying bases. Category differences delimit the argument rather than replace its central question. The paper concludes that the defensible object of a PP claim is a specific attributable contribution, not the total knowledge and intellect behind an AI-mediated artifact. This is a conceptual and normative proposal, not an empirical finding or a new legal entitlement.
Author: Barrera, Facundo
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221462962
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