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AI incident #829 ·

Facial Recognition System in Buenos Aires Triggers Police Checks Based on False Matches

Open on the AI Incident Database 1 news report Synced from the AIID API · record last edited 4 Sep 2026

What happened

Buenos Aires's facial recognition system mistakenly flagged innocent people as criminals, leading to wrongful stops and detentions. Judicial investigations indicate the technology may have been misused for unauthorized surveillance and data collection. Despite privacy risks, the system has been used widely without full disclosure of standards or safeguards,

Editor's notes (AI Incident Database)

Reconstruction of the timeline of events: (1) 2019: Buenos Aires implements a facial recognition system aimed at enhancing public safety, capturing thousands of individuals. (2) After implementation in 2019: At least 140 individuals, including Guillermo Ibarrola, are erroneously flagged as criminals due to database errors, leading to police checks and detentions. (3) 2020: The facial recognition feature is deactivated as a precaution during the COVID-19 pandemic and remains off by judicial order. (4) December 2023: Journalists confirm that their biometric data was accessed, which in turn prompted further scrutiny by them. (5) February 5, 2024: The Pulitzer Center publishes a report on the issues surrounding Buenos Aires's facial recognition system.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (1)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. How We Investigated Mass Surveillance in Argentina
    pulitzercenter.org · Karen Naundorf, Sarah Pabst · AIID #4245

Who was involved

Alleged harmed party
Privacy Guillermo Ibarrola General public of Argentina General public Buenos Aires residents Biometric data subjects
On AIID: Privacy, Guillermo Ibarrola, General public of Argentina, General public, Buenos Aires residents, Biometric data subjects

AI systems implicated

Surveillance technologyFacial recognition systemsAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

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...

    International AI Safety Report 2025 (Bengio2025)

  • 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.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • 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.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • 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.

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • 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)."

    AGI Safety Literature Review (Everitt2018 )

  • 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...

    AGI Safety Literature Review (Everitt2018 )

  • 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...

    AGI Safety Literature Review (Everitt2018 )

  • Misaligned consequentialist reasoning

    "As we think about even more intelligent and advanced AI assistants, perhaps outperforming humans on many cognitive tasks, the question of how humans can successfully control such an assistant looms large. To achieve the...

    The Ethics of Advanced AI Assistants (Gabriel2024)

Linked by AIID editors or by its text-similarity model.

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