Deterministic Labor-Transaction Interlocks and Sovereignty Defense Against Exploitative Crowdsourcing: Phase III Cryptographic Governance for AI Data Labeling, Content Creation, and Expressive Privacy

This monograph establishes a rigorous mathematical, statutory, and cryptographic governance framework—Phase III: Privacy and Expressive Sovereignty Defense—designed to eliminate structural labor exploitation and monopsonistic underpricing across AI data-labeling pipelines and digital content crowdsourcing platforms. Current crowdsourced AI training ecosystems systematically externalize cognitive, thermodynamic, and economic costs onto precarious human contributors through opaque algorithmic wage suppression, unauthorized secondary dataset ingestion, and uncompensated intellectual property extraction. To dismantle this asymmetric architecture, we formalize a deterministic labor-transaction interlock executing over the 64-bit Goldilocks prime field ($\mathbb{F}_p, p = 2^{64} - 2^{32} + 1$) within a Quantum-Safe Financial & Sovereign Zero-Knowledge Virtual Machine (QFS-zkVM) coupled with RTO P2.0 and Q-SAFA hardware-enforced provenance gates. Under this protocol, no human-annotated corpus, creative artifact, or expressive telemetry can traverse the PHOENIX_BRIDGE / PHOENIX_ROUTER ingestion boundary unless three zero-knowledge completeness conditions simultaneously resolve: (1) cryptographic proof of prior, explicit, non-coercive Human-in-Command (HIC) consent; (2) atomic pre-settlement of fair-market statutory compensation exceeding living-wage and creative-royalty floor invariants; and (3) strict enforcement of the No-Learn Covenant (NLL-v1.0-2026), mathematically prohibiting unauthorized foundation-model weight updates, shadow scraping, or derivative style extraction. Furthermore, we align these physical-layer interlocks with fiduciary oversight obligations (analogous to Delaware Caremark standards and pre-trade risk controls under SEC Rule 15c3-5), transforming expressive privacy and intellectual property valuation from unenforceable platform terms into immutable cryptographic invariants. Official Reserved DOI: 10.5281/zenodo.24097666 Zenodo Record URL: [https://doi.org/10.5281/zenodo.24097666](https://doi.org/10.5281/zenodo.24097666) Author: Hasebe, Yoko (長谷部 洋子) — Lead Researcher, Autonomous Infrastructure Systems, Autonomous Living & Physical Infrastructure Institute, Iwakuni, Yamaguchi, Japan License & Integrity Covenant: Creative Commons Attribution 4.0 International (CC BY 4.0) with Strict No-Learn Covenant (NLL-v1.0-2026) 【日本語要旨】 本論文(第3フェーズ:プライバシーと表現の主権防衛)は、AIデータラベリングおよびデジタルコンテンツ制作のクラウドソーシング市場に蔓延する構造的な低単価買い叩き、無断のAI学習・データ収奪、および表現者の権利侵害を根絶するための厳格な労働・取引基準と暗号学的ガバナンス基盤を定式化するものである。64ビットGoldilocks素体($\mathbb{F}_p, p = 2^{64}-2^{32}+1$)上のQFS-zkVMおよびRTO P2.0 / Q-SAFA境界制御により、(1)Human-in-Command(HIC)に基づく事前の明確な合意証明、(2)正当な金銭的対価の事前アトミック決済証明、(3)無断機械学習・二次流用を数学的に遮断する「Strict No-Learn Covenant (NLL-v1.0-2026)」の充足がゼロ知識証明によって検証されない限り、いかなる創作物・ラベリングデータもプラットフォーム境界(PHOENIX_BRIDGE / PHOENIX_ROUTER)を通過できない決定論的インターロックを確立し、プライバシーと表現の主権を不可侵の物理・暗号層基準として実装する。