Spaces:
Running
Running
| # 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 | |