Statutory Opt-In Architecture and International Treaty Framework: Criminalizing Unconsented AI Data Harvesting via Thermodynamic Provenance and Deterministic Cryptographic Interlocks (日本語副題参考:オプトイン原則の法制化モデル:AI学習における「事前合意なきデータ収集」を違法化する国際条約・法規制のフレームワーク)

This monograph establishes a rigorous statutory, constitutional, and international treaty framework mandating strict Opt-In governance for artificial intelligence data ingestion, formally classifying unconsented data scraping and unauthorized model training as actionable intellectual property misappropriation and statutory unjust enrichment. Rejecting legacy "opt-out" regimes and expansive fair-use/text-and-data-mining (TDM) exemptions that externalize creative and operational costs onto human originators, this work formulates a dual-layer enforcement architecture: (1) a binding multilateral treaty model requiring explicit, verifiable ex-ante cryptographic consent and fair financial compensation prior to corpus tokenization or gradient updates, and (2) a deterministic hardware and zero-knowledge cryptographic interlock built over the 64-bit Goldilocks prime field (Fₚ, p = 2⁶⁴ - 2³² + 1) within the QFS-zkVM and PHOENIX_ROUTER governance stack. By coupling Human-in-Command (HIC) statutory liability, Caremark-grade fiduciary oversight, and zero-knowledge provenance verification, the proposed framework mathematically blocks unverified training pipelines at the physical compute boundary—rendering unauthorized AI data extraction both legally untenable and cryptographically non-executable. Japanese Summary(日本語要旨): 本論文は、AI学習における「オプトアウト(事後拒否)」および無断スクレイピングを許容する現行の法制上の欠陥を排し、事前の明示的合意と正当な金銭的対価の支払いを必須とする「厳格なオプトイン原則(Statutory Opt-In Principle)」の法制化モデルおよび国際条約フレームワークを提示するものである。事前合意なきデータ収集・AI学習を明確に違法化(知的財産権侵害・不当利得・不正競争行為として厳罰化)する実体法規と、64ビットGoldilocks素体上のゼロ知識証明(QFS-zkVM)