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An Afghan refugee's asylum claim was denied by a U.S. agency due to a pronoun-swapping error introduced by an automated translation tool in her written statement. This incident highlights the risks of using machine translation in high-stakes immigration and legal contexts, where minor inaccuracies can lead to severe consequences for asylum seekers. Despite these risks, machine translation continues to be integrated into government and legal workflows.
A Pashto-speaking refugee's asylum claim was denied by a US agency for a discrepancy between oral and written recount of an event allegedly due to an error of their automated translation tool which swapped pronouns of her written statement from "I" to "we".
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
Accuracy and effectiveness of AI decisions and actions are dependent on group membership, where decisions in AI system design and biased training data lead to unequal outcomes, reduced benefits, increased effort, and alienation of users.
Also classified as
Immigration authorities and workflows relied on automated translation tools in high-stakes legal contexts without sufficient human oversight.
The automated translation tool failed to perform reliably, introducing a critical pronoun error that altered the meaning of the asylum application.
The translation tool performed poorly on Pashto, a low-resource language, compared to high-resource languages, leading to an unequal and highly detrimental outcome for an Afghan refugee.
AI system
Due to a decision or action made by an AI system
Unintentional
Due to an unexpected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The incident was caused by an unintentional translation error ('I' swapped to 'we') produced by a deployed machine translation tool during post-deployment use.
Added categories peak at 2: Minor (Epistemic Harm, Direct)
An Afghan refugee's asylum claim was denied in the US due to an automated translation tool error that swapped pronouns. While highlighting systemic risks of using automated tools in high-stakes government workflows, the incident has minor national security implications, primarily affecting administrative decision-making and human rights processes.
The machine translation system operates as a tool, performing a specific, well-defined function (translating text) based on direct input without autonomous planning or context retention.
Risk Level 2: High Risk. The report describes the use of AI translation tools in 'high-risk government decision-making,' specifically migration and asylum applications, which has significant implications for fundamental rights.
The primary function of the AI system was to translate written statements from Pashto to English to assist in asylum applications.
The report provides clear, firsthand testimony of a specific translation failure that directly resulted in a denied asylum claim, as well as broader context on how these tools are deployed in immigration systems.
None
Technology
Pronoun Translation Errors
Automated tool swapped 'I' pronouns to 'we' in the written statement.
Low-Resource Language Lag
AI tools for Pashto and Dari lag behind dominant high-resource languages.
Lack of Cultural Awareness
AI fails to translate metaphors, idioms, and regional colloquialisms.
Data Inputs
Limited Online Texts
Fewer texts available online for training Pashto translation models.
Missing Specialized Vocabulary
Lack of training data for specific terms like family relations and ranks.
Human Factors
Over-reliance on Automation
Treating machine translation as the ultimate solution instead of a helper.
Process and Methods
Lack of Human Verification
Failing to check machine-generated translations with human experts.
Management
Prioritizing Cost over Safety
Companies use neural translation to cut costs and boost productivity.
Regulatory Environment
Permissive Agency Policies
Immigration agencies independently choose to opt in to automated tools.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
