AI incident ·
ICE Facial Recognition App Mobile Fortify Reportedly Misidentified Woman Twice During Immigration Enforcement in Oregon
In brief
An AI system built by United States Department of Homeland Security and Facial recognition system developers and deployed by United States Immigration and Customs Enforcement, United States Customs and Border Protection and 3 others allegedly harmed Women and girls, Women and 7 others.
- Risk domain
- AI system safety, failures, and limitations
- Occurred
- Coverage
- 1 report
What happened
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's 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.
Laws that address this harm
Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.
- India DPDP Act
- Law on Artificial Intelligence (2025)
- Law No. 132/2025 on artificial intelligence
- EU AI Act
- Texas Responsible AI Governance Act (TRAIGA)
Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (1)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- United States Immigration and Customs Enforcement United States Customs and Border Protection National security and intelligence stakeholders Law enforcement Facial recognition system deployers
- Alleged harmed party
- Women and girls Women Unnamed woman detained by United States Immigration and Customs Enforcement Privacy Individuals subject to immigration enforcement Immigrants General public of the United States General public Biometric data subjects
AI systems implicated
Mobile FortifyFacial recognition systemsAI-enabled decision support systems
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- AI system safety, failures, and limitations
- Risk subdomain
- 7.3 Lack of capability or robustness
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 7.3.
- Reliability issues
"Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...
- Type 2: Bigger than expected
Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.
- Type 3: Worse than expected
AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.
- Ethics and Morality Issues
LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.
- Safe learning
"AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."
- Malign belief distributions
"Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...
- Meta-cognition
"Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...
- Technical and operational risks
"To date, technical limitations and vulnerabilities are present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach an AI system’s safety and ethical guardrails to e...
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- Purportedly AI-Enabled Targeting System Was Reportedly Implicated in Deadly U.S. Strike on Iranian Primary School
- Claude Code Agent Reportedly Deleted DataTalks.Club Production Infrastructure, Database, and Snapshots via Terraform
- Purportedly AI-Generated Sepsis Alert Reportedly Prompted Potentially Inappropriate IV Fluid Administration for a Dialysis Patient, Averted by Clinician Intervention
Other incidents involving United States Immigration and Customs Enforcement
Source record: incident #1353 on the AI Incident Database · all 1 report