When someone took the microphone at a city council meeting in a language that no one on the dais spoke, there used to be a specific kind of silence. A bilingual staff member half-rising from her chair with her notepad, a pause, and a look toward the rear of the room. In some American cities, that silence is beginning to fade, and instead of another person, a piece of software humming softly through earbuds is filling the void.
According to a recent survey by Wordly, an AI translation company, 31% of local governments have at least partially switched to AI tools, while 66% still rely on bilingual employees to close the gap. Despite the small sample size (just 117 agencies), the image is suggestive. The majority of the responding counties and cities have populations between 50,000 and 300,000, which is a type of mid-sized America with tight budgets and constantly changing demographics that no one had anticipated.
One city that has switched to using Wordly’s platform instead of in-person interpreters is San Jose. The business claims to have reduced expenses and increased accessibility for locals who speak little or no English. Another question that no one seems quite prepared to address in public is whether the experience is the same for the person receiving it.
Elizabeth Jourdin, an HR manager in Washoe County, Nevada, has seen this unfold in an unexpected variety of contexts. In addition to using the tool for public meetings, the county also uses it for training monolingual case managers, onboarding hard-of-hearing employees, providing language access during marriage ceremonies, food safety programs for restaurant owners, and standard front desk customer service. Spanish is still the most common second language, but more people are learning Korean, Tagalog, Vietnamese, Mandarin, Hindi, and Urdu. According to her, the county is operating under a multi-year language access policy that aims to improve staff support in addition to the technology rather than just replace it.
The final detail is important. Reading between the lines of her remarks gives the impression that those closest to this project are not quite as optimistic as the vendors. According to her, “one of the greatest lessons we learned is that there is not a one-size-fits-all solution,” and hedging is important. A deaf employee during an onboarding session may have different needs than a Spanish-speaking grandmother applying for a permit, and software that performs well in one scenario may perform poorly in another.
Naturally, the field of machine translation as a whole has taken off. Timelines that once took hours have been reduced by Google Translate, DeepL, Microsoft, Amazon, Reverso, and now conversational systems based on large language models. Researchers studying legal translation have spent the last few years discreetly examining whether human translators are still required in their field, and the results are conflicting in ways that marketing brochures typically ignore. Boilerplate is expertly handled by AI. Idiom, tone, and the subtle cultural nuances that make a sentence seem respectful or patronizing still cause it to falter.
It’s difficult not to wonder what is lost when the bilingual employee with the notepad leaves the room when this is happening in the public sector. The price decreases. Coverage increases. Another, more difficult to identify, change in the way a government communicates with its constituents. Across the nation, the experiment is being conducted in real time at food safety classes, zoning hearings, and marriage ceremonies. As of right now, the results are still being received.
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