Deterministic Fair-Pricing and Anti-Monopsony Minimum Royalty Architecture for AI Training Data: Information-Theoretic Scarcity, Marginal Contribution Valuation, and QFS-zkVM Cryptographic Settlement

Autonomous Infrastructure & Deterministic Quantum Systems Monograph Series Reserved Official DOI: 10.5281/zenodo.24096623 Author: Hasebe, Yoko (長谷部 洋子) Affiliation: 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) [Abstract] Uncompensated scraping and monopsonistic underpricing of human creative works by large-scale AI platform operators constitute a structural market failure and systematic intellectual property extraction. Existing flat-rate licensing schemes fail to capture either the non-fungible originality of individual datasets or their true causal impact on downstream model capabilities, enabling predatory price suppression. This monograph establishes a rigorous mathematical and cryptographic framework for fair AI training data valuation and anti-monopsony royalty enforcement. First, we formalize an Information-Theoretic Scarcity Index derived from Kullback-Leibler divergence and semantic novelty relative to baseline corpora, paired with a game-theoretic Marginal Contribution Estimator grounded in Shapley-value gradient attribution. Second, we prove a non-negotiable Minimum Royalty Floor algorithm that sets an enforceable lower bound on creator compensation, mathematically precluding below-value buyouts and coercive micro-pricing. Third, to guarantee prior explicit consent and atomic financial settlement before any parameter update occurs, we integrate this valuation engine into a deterministic hardware-cryptographic interlock executing over the 64-bit Goldilocks prime field (F_p, p = 2^64 - 2^32 + 1) via QFS-zkVM, Q-SAFA, RTO P2.0, and PHOENIX_BRIDGE / PHOENIX_ROUTER under strict Human-in-Command (HIC) governance. Finally, we align pre-ingestion settlement verification with corporate fiduciary oversight duties under Delaware Caremark standards, establishing an auditable zero-knowledge proof protocol that renders unauthorized training or royalty evasion cryptographically and legally unviable. 【日本語要旨】 AI学習データの適正価格算定モデル:創作物の希少性と寄与度に基づき、買い叩きを防ぐための最低ロイヤリティ算出アルゴリズム 大規模AI事業者による創作物の無断学習および買い占め構造(モノプソニー)