# Model Retirement: AI Governance & Safety

## Public-safe source report summary

This uploaded source report is preserved as durable project evidence for Cognivirus.com. It contributes concepts to the v1.15.0 danger-model expansion: Model retirement, zombie models, decommissioning.

## Evidence handling

This is treated as a **source dossier**, not as independently verified empirical consensus. Public pages may use it after applying the site evidence ladder, metaphor boundaries, and non-operational safety policy. It must not be used to claim that AI systems are conscious, literal biological viruses, or inevitably catastrophic.

## Concepts extracted for the site

- The unsafe unit may be a transition graph rather than one model artifact.
- Local component approval does not prove runtime-composition safety.
- Evidence should name the exact carrier, route, memory state, evaluator, tool profile, and promotion rule involved.
- Observable outcomes need replayable traces rather than trust language.
- Retirement, rollback, and behavioral-extinction reviews must include data, memory, synthetic examples, descendants, aliases, and human workflows.

## Source orientation

Model Retirement: AI Governance & Safety Executive Summary: Model retirement is the controlled decommissioning of AI models that are obsolete, unsafe or underperforming. It ensures that legacy models are removed from production with planning, validation, and auditability. This practice is vital for safety, trust and compliance – a faulty or biased model hurts revenue and brand, and can violate regulations. Effective retirement policies define triggers (like performance drift or policy violations), metrics and processes to gracefully retire models, archive artifacts, and notify stakeholders. This report covers definitions, technical triggers (

## Site interpretation

The report is used to deepen public and technical explanations of distributed behavioral persistence, synthetic-feedback risk, action-layer controls, observability, lineage, diversity, promotion pressure, and retirement failure. It does not authorize exploit instructions, self-replication recipes, credential workflows, or backdoor construction guidance.