Formal Governance for Trustworthy AI Systems. Explicit responsibility mapping, deterministic escalation, and complete auditability.
AI may inform decisions, but responsibility always belongs to humans or institutions. This is not a guideline. It is a hard invariant that cannot be bypassed, delegated, or automated away.
Most AI systems fail not because models are wrong, but because:
Every AI interaction must explicitly declare these roles. No exceptions.
Pattern Recognition
No authority
No enforcement
No final judgment
Final Authority
Cannot be removed
Cannot be bypassed
Rule Definition
Deterministic
Auditable
Legal Ownership
Cannot blame AI
Clear boundaries between what AI must never do and what humans must always retain.
AI provides recommendations, not judgments.
No model output constitutes approval or enforcement.
Accountability cannot be transferred to software.
Automation of analysis does not equal automation of authority.
Low confidence must trigger escalation, not silence.
Humans decide. AI informs. This order is immutable.
Context, intent, and values cannot be delegated to patterns.
Humans can reject AI recommendations without penalty.
Institutions own outcomes. "The AI did it" is not a defense.
Every AI recommendation must be interpretable and auditable.
Most AI failures occur not from model errors, but from unclear responsibility mapping:
Humans defer to machines
Institutions blame "the algorithm"
High-risk decisions slip through
Traditional (Vague)
Mathematical (Precise)
Three tiers of governance documentation
Public manifesto with philosophical and mathematical grounding
Download PDFRoles, escalation logic, and high-level schemas for regulators
Download PDFFull matrices, schemas, audit mappings, and export packages
Download PDF
"If you cannot answer 'who is accountable when this fails?'
you are not ready to deploy."
This framework ensures that question always has a human answer.