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Incident 1353: ICE Facial Recognition App Mobile Fortify Reportedly Misidentified Woman Twice During Immigration Enforcement in Oregon

Description: During an immigration enforcement operation in Oregon, U.S. Immigration and Customs Enforcement (ICE) officers reportedly used the facial recognition application Mobile Fortify to identify a detained woman. The system reportedly returned two different and incorrect identities for the same individual across separate scans.
Editor Notes: Timeline note: The incident ID date of 10/15/2025 is an approximation. The precise day of the misidentification incident in Oregon is not publicly reported, but multiple sources indicate that the incident occurred in October 2025. Mobile Fortify was reportedly integrated into ICE operations on May 20, 2025. The 404 Media report first documenting the misidentification was published on January 19, 2026, and the corresponding incident ID was created on January 31, 2026.

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Alleged: United States Department of Homeland Security developed an AI system deployed by United States Immigration and Customs Enforcement and United States Customs and Border Protection, which harmed Individuals subject to immigration enforcement and Unnamed woman detained by United States Immigration and Customs Enforcement.
Alleged implicated AI system: Mobile Fortify

Incident Stats

Incident ID
1353
Report Count
1
Incident Date
2025-10-15
Editors
Daniel Atherton
Applied Taxonomies
MIT

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

7.3. Lack of capability or robustness

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. AI system safety, failures, and limitations

Entity

Which, if any, entity is presented as the main cause of the risk
 

AI

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Unintentional

Incident Reports

Reports Timeline

Incident OccurrenceICE’s Facial Recognition App Misidentified a Woman. Twice
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ICE’s Facial Recognition App Misidentified a Woman. Twice

ICE’s Facial Recognition App Misidentified a Woman. Twice

404media.co

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ICE’s Facial Recognition App Misidentified a Woman. Twice
404media.co · 2026

When authorities used Immigration and Customs Enforcement's (ICE) facial recognition app on a detained woman in an attempt to learn her identity and immigration status, it returned two different and incorrect names, raising serious question…

Variants

A "variant" is an AI incident similar to a known case—it has the same causes, harms, and AI system. Instead of listing it separately, we group it under the first reported incident. Unlike other incidents, variants do not need to have been reported outside the AIID. Learn more from the research paper.
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