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Philanthropy Refugee Translations
Tarjimly co-founder Atif Javed presents his app at the Google Impact Summit on Wednesday, Sept. 4, 2024, in Sunnyvale, Calif. (AP Photo/Juliana Yamada)

A volunteer network of interpreters wants to make refugees' languages more accessible. Will AI help?

Refugees navigating resettlement often face a shared hurdle: poor machine translations and a short supply of interpreters knowledgeable in their less-serviced languages

By JAMES POLLARD
Published - Sep 19, 2024, 09:03 AM ET
Last Updated - Dec 16, 2024, 07:05 PM EST

NEW YORK (AP) — They may be Tigrinya speakers fleeing the authoritarian Eritrean government's indefinite military service policy. Or Rohingya people escaping ethnic violence in Myanmar. But refugees navigating resettlement often face a shared hurdle: poor machine translations and a short supply of interpreters knowledgeable in their less-serviced languages.

Tarjimly, a Google-backed nonprofit described as “Uber for translators,” aims to help asylum seekers clear that hurdle. Through a new artificial intelligence partnership, Tarjimly trains outside large language models while allowing its volunteers to respond more urgently to needs for translators. It's a feedback loop where humans teach the nuances of each language to the machines by sharing data from one-on-one calls and correcting automated translations.

And it's this uniquely human realm of language that Tarjimly co-founder Atif Javed believes exemplifies the ever-tricky balance between individuals' ingenuity and technological advancement. He says it's the needed personal touch that shows why AI's rapid development shouldn't generally stoke widespread fears.

Languages popular in the Global South — such as the Dari and Pashto commonly spoken in Afghanistan, home to one of the world’s largest protracted refugee crises — have the worst quality coverage, according to Javed. He feels well positioned to supplement the internet's English-dominated information troves that train services like Google Translate with his mobile app's more diverse data sets.

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