Creative Authorship and Structural Indeterminacy: Attribution-Prior Governance in AI-Assisted Art Production

A companion paper established that the Accountability Incompleteness Theorem formally predicts the attribution failures courts repeatedly encounter in AI governance across three legal domains. Creative authorship in AI-assisted work was identified as a fourth candidate domain requiring its own architectural grounding. This paper provides that grounding. The structural problem in creative authorship disputes is precisely analogous: causal attribution of a work's origin is required for legal and institutional purposes — copyright, moral rights, authorship credit, market legitimacy — but the relevant causal variable is upstream of any observable data. Output inspection, generation logs, percentage-of-AI-involvement metrics, and stylistic analysis all operate on the surface of the problem without reaching the causal question. This paper maps the condition onto the Accountability Incompleteness Theorem's Axiom 1 (Attributability) failure and the Latent-Space Structural Probe Domain's Representation Indeterminacy condition. It then presents the Systemic Discipline for Cognitive Architecture (SDCA v2.0) as a concrete instantiation of attribution-prior governance in the creative domain — an architecture designed from the problem rather than patched onto it. The paper advances a minimum definition of art adequate to the governance problem, identifies why current industry approaches address the wrong causal variable, and demonstrates how SDCA's gate architecture encodes the correct one.