In 2020, Uma Mirkhail got a firsthand demonstration of how damaging a bad translation can be.
A crisis translator specializing in Afghan languages, Mirkhail was working with a Pashto-speaking refugee who had fled Afghanistan. A U.S. court had denied the refugee’s asylum bid because her written application didn’t match the story told in the initial interviews.
In the interviews, the refugee had first maintained that she’d made it through one particular event alone, but the written statement seemed to reference other people with her at the time — a discrepancy large enough for a judge to reject her asylum claim.
After Mirkhail went over the documents, she saw what had gone wrong: An automated translation tool had swapped the “I” pronouns in the woman’s statement to “we.”
Mirkhail works with Respond Crisis Translation, a coalition of over 2,500 translators that provides interpretation and translation services for migrants and asylum seekers around the world. She told Rest of World this kind of small mistake can be life-changing for a refugee. In the wake of the Taliban’s return to power in Afghanistan, there is an urgent demand for crisis translators working in languages such as Pashto and Dari. Working alongside refugees, these translators can help clients navigate complex immigration systems, including drafting immigration forms such as asylum applications. But a new generation of machine translation tools is changing the landscape of this field — and adding a new set of risks for refugees.
Machine translation has been on the rise since the introduction of neural network techniques, similar to those used in generative artificial intelligence. In 2016, Google launched its first neural machine translation system. Today, when subtitling films for streaming companies or drafting documents for law firms, some of the most established global translation companies use neural machine translation in their workflow in an effort to cut costs and boost productivity. But like the new generation of AI chatbots, machine translation tools are far from perfect, and the errors they introduce can have severe consequences.
Companies working in this space generally recognize the danger of pure automation, and insist that their tools be used only under close human supervision. “Machine-learning translations are not yet in a place to be trusted completely without human review,” said Sara Haj-Hassan, chief operations officer of Tarjimly, a nonprofit startup that connects refugees and asylum seekers with human volunteer translators and interpreters, to Rest
