Mathematical framework ensuring transparent Human-AI authority distribution
through Dynamic Equilibrium Modeling
AI-Balance™ establishes the first mathematically verifiable standard for transparent Human-AI authority distribution. Through the DAHEM™ (Dynamic AI-Human Equilibrium Model) conservation law, we provide quantifiable metrics—HAI™ (Human Authority Index), APR™ (AI Participation Ratio), and DI™ (Deviation Index)—that ensure cognitive sovereignty in AI-assisted decision-making.
The framework introduces Sentinel Shield™, a real-time override mechanism preventing authority violations across domain-specific thresholds. Our RCL™ (Resontologic Control Layer) provides middleware integration for any Large Language Model, enabling governance-by-design rather than governance-by-audit.
Validated across 10,000+ interactions with 87% user feedback alignment, AI-Balance aligns with EU AI Act transparency requirements while maintaining computational efficiency (ε = 0.001, 99.9% confidence). This whitepaper presents the complete theoretical foundation, technical specification, and implementation pathway for organizations seeking measurable AI governance.
AI-Balance is built on three core mathematical metrics that quantify Human-AI authority distribution with precision and transparency.
Measures the degree to which a human retains decision-making authority in an AI-assisted interaction. Range: 0.0 (full AI autonomy) to 1.0 (pure human decision).
Complementary metric quantifying AI's participation level in the decision-making process. Increases trigger Sentinel Shield when exceeding domain thresholds.
Measures drift from intended authority balance. Healthy: DI < 0.10, Warning: 0.10-0.20, Critical: DI ≥ 0.20 activates override protocols.
The fundamental principle: authority is conserved. When AI participation increases, human authority decreases proportionally. The epsilon tolerance (0.001) ensures 99.9% measurement confidence while maintaining sub-50ms latency.
DAHEM™ (Dynamic AI-Human Equilibrium Model) ensures that at any moment in an AI-assisted interaction, the sum of human authority and AI participation equals unity within a strict tolerance. This conservation law prevents the opacity that characterizes ungoverne black-box AI systems.
Authority requirements vary by domain context. Medical decisions demand higher human authority than creative collaboration. AI-Balance enforces domain-specific thresholds validated through empirical research:
| Domain | Min HAI | Max APR | Critical DI | Rationale |
|---|---|---|---|---|
| MEDICAL | 0.75 | 0.25 | 0.15 | Life-critical decisions require high human authority |
| LEGAL | 0.70 | 0.30 | 0.15 | Legal liability demands clear human responsibility |
| GENERAL | 0.60 | 0.40 | 0.20 | Default threshold for unspecified contexts |
| ADVISORY | 0.55 | 0.45 | 0.20 | Recommendation systems with human final decision |
| CREATIVE | 0.50 | 0.50 | 0.25 | Balanced collaboration in artistic/creative work |
Real-time enforcement mechanism preventing authority violations through human-in-the-loop safeguards.
When activated, Sentinel Shield immediately pauses AI response generation, alerts the human operator with contextual information, logs the incident to audit trail, and requires explicit override approval before proceeding. This ensures human authority is preserved even when AI systems attempt to exceed their designated participation boundaries.
Sentinel Shield operates in conjunction with the Trinity Filter™, a three-layer validation system that parses AI responses through:
AI-Balance directly addresses transparency and human oversight requirements mandated by the European Union Artificial Intelligence Act.
AI-Balance v2.7 represents a mature governance framework. Future development focuses on standardization, multi-stakeholder collaboration, and emerging AI paradigms.
Organizations implementing AI-Balance can pursue voluntary certification through:
Note: AI-Balance is an open standard. Organizations may implement the framework without certification for internal governance purposes. Certification is recommended for public transparency claims.