Generative Ownership Framework

An accurate record of what artificial intelligence produced need not establish what it originated, how it was governed, or what claims to authorship or ownership are justified. This article develops the Generative Ownership Framework (GOF), a causal and ethical framework for human-AI creation. It separates creative-process analysis, governance, evidence and inference, and attributive and normative evaluation. Creative-process analysis distinguishes production, origination, generative development, and cognitive-access or representational roles that recover, stabilize, inspect, work with, or express antecedent human structure. Origination may be human, machine, or distributed and may differ across scales. The relevant scale should be determined by the contribution and causal question, not adjusted retrospectively to favor an attribution. GOF treats causal attribution as claim-relative. Evidence must discriminate among materially different causal histories; where it cannot, provenance may remain unresolved. Historical continuity can support antecedent human structure without establishing later development, while changes in fluency, polish, or formalization do not by themselves identify provenance. Human authorship has an affirmative creative basis when a person’s relationship to the claimed work is work-specific, causally operative, and materially formative. Production volume, causal importance, control, responsibility, or time spent are not sufficient by themselves. Runtime cognitive tuning and cognitive conversion cost describe how human contribution can shape generative conditions or redistribute representational work without settling origination. GOF recognizes machine contribution while rejecting automatic inferences from machine participation to diminished human authorship or fractionalized ownership. Disclosure, pedigree, and inspectability should not substitute for contribution-specific evidence, and cognitive provenance remains distinct from epistemic validity.