AI incident #76 ·

Buenos Aires Government Reportedly Used Children's Personal Data in Facial Recognition System for Fugitives

Open on the AI Incident Database 1 news report Synced from the AIID API · record last edited 3 Sep 2026

What happened

Beginning in April 2019, the Buenos Aires city government reportedly used data from Argentina’s CONARC fugitive database, including children’s identities and reference photos, in its live Facial Recognition System for Fugitives (SRFP). Human Rights Watch found at least 166 children had appeared in CONARC between 2017 and 2020 and warned that the system exposed them to privacy violations and elevated risks of false matches.

Editor's notes (AI Incident Database)

Incident 829 (https://incidentdatabase.ai/cite/829/) documents broader alleged facial-recognition misuse resulting in wrongful stops and detentions involving the same Buenos Aires facial recognition system. Incident 76 is limited to the system's use of children's personal and biometric data and the associated privacy and false-match risks.

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.

Who was involved

Alleged harmed party
Privacy Minors General public of Buenos Aires General public of Argentina General public Buenos Aires children Biometric data subjects
On AIID: Privacy, Minors, General public of Buenos Aires, General public of Argentina, General public, Buenos Aires children, Biometric data subjects

AI systems implicated

UltraIPSurveillance technologySistema de Reconocimiento Facial de Prófugos (SRFP)Law enforcement facial recognition systemsFacial recognition systemsConsulta Nacional de Rebeldías y Capturas (CONARC)Buenos Aires live facial recognition systemBiometric data processing systemsAI identification and tracking systemAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Intentional
Timing
Post-deployment
Harm level
AI tangible harm event
Sectors
law enforcement, public administration
Countries
AR

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 2.1.

  • Risks to privacy

    "General- purpose AI models or systems can ‘leak’ information about individuals whose data was used in training. For future models trained on sensitive personal data like health or financial data, this may lead to partic...

    International Scientific Report on the Safety of Advanced AI (Bengio2024)

  • Risks to privacy

    "General- purpose AI systems can cause or contribute to violations of user privacy. Violations can occur inadvertently during the training or usage of AI systems, for example through unauthorised processing of personal d...

    International AI Safety Report 2025 (Bengio2025)

  • Privacy Leakage

    "Privacy Leakage means the generated content includes sensitive personal information"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Private Training Data

    "As recent LLMs continue to incorporate licensed, created, and publicly available data sources in their corpora, the potential to mix private data in the training corpora is significantly increased. The misused private d...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Memorization in LLMs

    "Memorization in LLMs refers to the capability to recover the training data with contextual prefixes. According to [88]–[90], given a PII entity x, which is memorized by a model F. Using a prompt p could force the model...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Association in LLMs

    "Association in LLMs refers to the capability to associate various pieces of information related to a person. According to [68], [86], given a pair of PII entities (xi , xj ), which is associated by a model F. Using a pr...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Privacy Leakage

    "The model is trained with personal data in the corpus and unintentionally exposing them during the conversation."

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Privacy and regulation violations

    "Some of the broken systems discussed above are also very invasive of people’s privacy, controlling, for instance, the length of someone’s last romantic relationship [51]. More recently, ChatGPT was banned in Italy over...

    Navigating the Landscape of AI Ethics and Responsibility (Cunha2023)

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

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