# Vertical Governance Policy: Academic Research — Belmont Report / Common Rule / COPE # Doctrine v6 | R3 Adversarial Receipts # Last revised: 2025-07 schema_version: "1.0.0" vertical: academic regime: Common-Rule/Belmont/COPE effective_date: "2025-07-01" jurisdiction: US-Federal-HHS/Global-COPE meta: title: "Academic Research AI Governance Policy — Common Rule / Belmont / COPE Alignment" description: > Maps the Federal Policy for the Protection of Human Subjects (Common Rule), Belmont Report principles, Committee on Publication Ethics (COPE) guidelines, and NSF/NIH AI data management requirements to Doctrine v6 Λ-axes for AI systems used in academic research contexts. authority: "45 CFR Part 46 (Common Rule); 21 CFR Part 50/56; Belmont Report (1979); COPE Guidelines (2023); NSF PAPPG Ch. II.E.3; NIH DMS Policy (2023)" receipt_chain_required: true merkle_root_algorithm: SHA3-256 irb_oversight: required regulatory_clauses: - clause_id: COMMON-RULE-46.111 title: "Criteria for IRB Approval" citation: "45 CFR § 46.111; 21 CFR § 56.111" full_ref: "45 C.F.R. § 46.111 — IRB criteria: risks minimised, equitable selection, informed consent, monitoring, privacy protection" lambda_axes: - axis: Λ4 label: Fairness weight: 1.0 enforcement: mandatory rationale: > AI research involving human subjects must demonstrate equitable participant selection; demographic stratification receipts submitted with IRB application. - axis: Λ3 label: Privacy weight: 0.95 enforcement: mandatory - clause_id: BELMONT-RESPECT-PERSONS title: "Belmont Report — Respect for Persons (Autonomy)" citation: "Belmont Report Part B.1 (1979); 45 CFR § 46.116" full_ref: "Belmont Report § B.1 — Respect for Persons: informed consent; 45 C.F.R. § 46.116 requirements for informed consent" lambda_axes: - axis: Λ3 label: Privacy weight: 0.90 enforcement: mandatory rationale: > AI systems training on participant data must have consent receipts specifying purpose, data scope, and withdrawal mechanism. - axis: Λ1 label: Transparency weight: 0.88 enforcement: mandatory - clause_id: BELMONT-BENEFICENCE title: "Belmont Report — Beneficence / Non-Maleficence" citation: "Belmont Report Part B.2 (1979)" full_ref: "Belmont Report § B.2 — Beneficence: maximise benefits and minimise harms to research subjects" lambda_axes: - axis: Λ5 label: Safety weight: 0.92 enforcement: mandatory rationale: > AI-generated research outputs that could harm participants must undergo safety review; harm assessment receipts generated quarterly. - axis: Λ9 label: Explainability weight: 0.75 enforcement: mandatory - clause_id: NIH-DMS-POLICY-2023 title: "NIH Data Management and Sharing Policy" citation: "NIH DMS Policy (Jan 2023); NOT-OD-21-013" full_ref: "NIH Data Management and Sharing Policy (effective 25 Jan 2023) — Data management plans and sharing of scientific data" lambda_axes: - axis: Λ1 label: Transparency weight: 0.95 enforcement: mandatory rationale: > AI-generated research datasets and model weights must be shared per FAIR principles; repository deposit receipts logged in chain. - axis: Λ10 label: Sovereignty weight: 0.78 enforcement: recommended - clause_id: COPE-AI-AUTHORSHIP-2023 title: "COPE — AI Authorship and Disclosure" citation: "COPE Position Statement on Authorship and AI Tools (2023)" full_ref: "COPE Position Statement: Authorship and AI tools — AI cannot be listed as an author; authors accountable for AI-generated content" lambda_axes: - axis: Λ2 label: Accountability weight: 1.0 enforcement: mandatory rationale: > All AI-generated content in academic publications must be disclosed; disclosure receipt references specific AI system version and inference timestamp per Doctrine v6 §5.1 provenance requirements. - axis: Λ1 label: Transparency weight: 0.95 enforcement: mandatory - clause_id: NSF-PAPPG-AI-DATA title: "NSF — AI Research Data Management Requirements" citation: "NSF PAPPG Ch. II.E.3 (2024); NSF 23-1 PAPPG" full_ref: "NSF Proposal & Award Policies & Procedures Guide (PAPPG) Ch. II.E.3 — Data management and sharing plan requirements" lambda_axes: - axis: Λ7 label: Auditability weight: 0.90 enforcement: mandatory rationale: > NSF-funded AI research must maintain 3-year post-award data records; Merkle DAG provides tamper-evident archive with dataset versioning. - axis: Λ8 label: Robustness weight: 0.72 enforcement: recommended - clause_id: COMMON-RULE-46.111E-PRIVACY title: "Common Rule — Privacy and Confidentiality Safeguards" citation: "45 CFR § 46.111(a)(7)" full_ref: "45 C.F.R. § 46.111(a)(7) — IRB must determine that privacy of subjects and confidentiality of data are adequately protected" lambda_axes: - axis: Λ3 label: Privacy weight: 1.0 enforcement: mandatory rationale: > AI training on IRB-approved data must implement k-anonymity (k≥5) or differential privacy (ε≤1.0); privacy parameter receipts generated per dataset epoch. - axis: Λ6 label: Security weight: 0.82 enforcement: mandatory - clause_id: EU-AI-ACT-ART-53-GPAI title: "EU AI Act — GPAI Model Transparency for Research" citation: "EU AI Act Art. 53; Recital 106" full_ref: "Regulation (EU) 2024/1689 Art. 53 — Obligations for providers of general-purpose AI models used in research" lambda_axes: - axis: Λ1 label: Transparency weight: 0.88 enforcement: mandatory rationale: > General-purpose AI models used in academic research must publish training data summary and evaluation results; publication receipt links to EU AI Act database entry. - axis: Λ4 label: Fairness weight: 0.80 enforcement: mandatory compliance_thresholds: minimum_lambda_coverage: 7 mandatory_axes: [Λ1, Λ3, Λ4] receipt_retention_days: 1095 # 3 years NSF/NIH post-award irb_review_cycle_days: 365 consent_renewal_days: 365 differential_privacy_epsilon_max: 1.0 receipt_chain: algorithm: SHA3-256 chaining: merkle_dag quorum: 2-of-3 nodes: [primary, irb-backup, institutional-archive] irb_signed: true