RFC-031: Universal Humanity AI Governance — The Capstone Unification — 1. Problem Statement
AIGP Specification › RFC-031: Universal Humanity AI Governance — The Capstone Unification › 1. Problem Statement
Section index · 2. Universal Principles →
RFC-031: Universal Humanity AI Governance — The Capstone Unification
PRIVATE AND PROPRIETARY — NOT A PUBLIC RFC. Owned by Kanjani AI Research & Causum. See NOTICE.md.
Status: DRAFT
Authors: Kanjani AI Research & Causum
Date: 2026-06-17
1. Problem Statement
AI governance is fragmenting along jurisdictional lines. Every major economic bloc, cultural tradition, and political system is producing its own governance framework — each legitimate, each reflecting genuine values, and each potentially incompatible with the others. This fragmentation creates an impossible situation for:
- Global AI systems that must operate across jurisdictions simultaneously
- Developers who cannot build separate governance implementations for every framework
- Organizations that operate internationally and need coherent governance posture
- Individuals whose rights should not depend on which jurisdiction’s framework their AI interaction happens to traverse
- AI agents that cross jurisdictional boundaries in milliseconds during execution
The existing frameworks addressed by prior AIGP RFCs include:
| Framework | RFC | Primary Orientation |
|---|---|---|
| International Humanitarian Law | RFC-027 | Protection of persons in conflict |
| EU AI Act | RFC-028 | Risk-based prohibition and compliance |
| AU Continental AI Strategy | RFC-029 | Equitable development and sovereignty |
| Japan AI Promotion Act | RFC-030 | Innovation enablement and promotion |
| UNESCO AI Ethics Recommendation | RFC-031 | Universal ethical principles |
| NIST AI RMF | RFC-031 | Risk management methodology |
| US Executive Order 14110 | RFC-031 | Safety, security, and trustworthiness |
| China Interim Measures for Generative AI | RFC-031 | Content governance and state alignment |
Design Note: Frameworks RFC-027 through RFC-030 have standalone jurisdictional contexts with their own
declare*Context()SDK methods and server-side rule engines because they represent enforceable jurisdictional regimes — each with a specific geographic/legal scope and mandatory compliance requirements.UNESCO, NIST, EO 14110, and China’s Interim Measures are synthesized INTO RFC-031’s universal rules (UH-001 through UH-010) rather than implemented as separate contexts because they are either: (a) advisory/guidance frameworks without direct enforcement mechanisms (UNESCO, NIST), (b) executive directives that delegate to existing agencies (EO 14110), or (c) content governance regimes that map to existing transparency and accountability rules (China). Their principles are fully absorbed — see Section 2 for the per-principle framework mapping.
Additionally, the Dual-Governance Reinforcement Model (Kanjani AI Research & Causum, 2026) provides the theoretical foundation that explains WHY governance must be structured as a control system rather than a static policy document.
1.1 The Unification Problem
These frameworks are not merely different — they embody fundamentally different assumptions:
- The EU AI Act assumes AI is potentially harmful and must be constrained (prohibition-first)
- The Japan AI Promotion Act assumes AI is beneficial and must be enabled (promotion-first)
- The AU Continental AI Strategy assumes AI must serve collective development (equity-first)
- IHL assumes AI operates in contexts where human life is at immediate stake (protection-first)
- NIST AI RMF assumes risk can be managed through systematic methodology (process-first)
- China’s Interim Measures assume AI must align with social stability (alignment-first)
A universal framework cannot simply take the intersection of these (which would be nearly empty) or the union (which would be contradictory). Instead, it must identify the invariant principles that ALL frameworks share — the governance atoms that every tradition recognizes as necessary, regardless of how they weight or prioritize them.
1.2 The Control System Insight
The Dual-Governance Reinforcement Model provides the key insight: governance is not a set of rules but a stabilizing control system for hybrid human-machine cognition. This means:
- Governance must have feedback loops (not just forward constraints)
- Governance must adapt to real-world outcomes (not remain static)
- Governance must address bidirectional risk (AI→Human AND Human→AI distortion)
- Governance must preserve human authority as the non-transferable accountability anchor
These properties are framework-independent. They apply whether the governance orientation is prohibition-first, promotion-first, equity-first, or protection-first.