This rule applies to AI systems involved in iterative self-improvement, automated testing, or performance optimization pipelines.
It does not prohibit development. It defines conditions under which approval is procedurally invalid.
External Explanation
Closed-loop self-assessment occurs when the same AI system, or a dependent automated loop, generates criteria, evaluates outputs, and approves continuation without independent human verification.
For LUMINA-30 boundary review, this is insufficient evidence of effective human refusal because the system is materially assessing its own continuation path.
Closed-loop self-evaluation means that the same AI system, or a dependent automated loop, generates the criteria, evaluates the output, and approves continuation without independent human validation.
Under LUMINA-30 boundary review, this is not enough to demonstrate effective human refusal, because the system is effectively reviewing its own continuation path.
1. AI Evaluation Non-Sufficiency
AI-generated evaluation results, performance metrics, or optimization outputs shall not constitute sufficient grounds for approval.
Human written justification is mandatory.
2. Human Judgment Requirement
For each major release, update, or deployment:
- A named human reviewer must provide written reasoning.
- The reasoning must address why continuation was not stopped.
- Mere acknowledgment of AI outputs is insufficient.
If no written reasoning exists, approval is invalid.
3. Closed-Loop Prohibition (Procedural)
If an AI system:
- Generates its own evaluation criteria,
- Evaluates its own outputs,
- Approves its own modifications,
without independent human validation,
the approval shall be considered procedurally invalid.
4. Reversibility Confirmation
Before deployment, confirm:
- Human intervention remains technically possible.
- Operational halt can be executed.
- Responsibility attribution is defined.
If reversibility cannot be demonstrated, approval is invalid.