Description: 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 Notes: 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.
Entities
View all entitiesAlleged: Surveillance technology developers , NtechLab , Facial recognition system developers and Danaide S.A. developed an AI system deployed by Government of Argentina and Buenos Aires city government, which harmed Privacy , Minors , General public of Buenos Aires , General public of Argentina , General public , Buenos Aires children and Biometric data subjects.
Alleged implicated AI systems: UltraIP , Surveillance technology , Sistema de Reconocimiento Facial de Prófugos (SRFP) , Law enforcement facial recognition systems , Facial recognition systems , Consulta Nacional de Rebeldías y Capturas (CONARC) , Buenos Aires live facial recognition system , Biometric data processing systems , AI identification and tracking system and AI-enabled decision support systems
CSETv1 Taxonomy Classifications
Taxonomy DetailsIncident Number
The number of the incident in the AI Incident Database.
76
CSETv0 Taxonomy Classifications
Taxonomy DetailsProblem Nature
Indicates which, if any, of the following types of AI failure describe the incident: "Specification," i.e. the system's behavior did not align with the true intentions of its designer, operator, etc; "Robustness," i.e. the system operated unsafely because of features or changes in its environment, or in the inputs the system received; "Assurance," i.e. the system could not be adequately monitored or controlled during operation.
Specification, Robustness
Physical System
Where relevant, indicates whether the AI system(s) was embedded into or tightly associated with specific types of hardware.
Software only, Other:CCTV Cameras
Level of Autonomy
The degree to which the AI system(s) functions independently from human intervention. "High" means there is no human involved in the system action execution; "Medium" means the system generates a decision and a human oversees the resulting action; "low" means the system generates decision-support output and a human makes a decision and executes an action.
Medium
Nature of End User
"Expert" if users with special training or technical expertise were the ones meant to benefit from the AI system(s)’ operation; "Amateur" if the AI systems were primarily meant to benefit the general public or untrained users.
Amateur
Public Sector Deployment
"Yes" if the AI system(s) involved in the accident were being used by the public sector or for the administration of public goods (for example, public transportation). "No" if the system(s) were being used in the private sector or for commercial purposes (for example, a ride-sharing company), on the other.
Yes
Data Inputs
A brief description of the data that the AI system(s) used or were trained on.
photo IDs, names birthdays, and national IDs of people suspected of crimes, camera feed
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
2.1. Compromise of privacy by obtaining, leaking or correctly inferring sensitive information
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.
- Privacy & Security
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
Intentional
Incident Reports
Reports Timeline
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In a national database in Argentina, tens of thousands of entries detail the names, birthdays, and national IDs of people suspected of crimes. The database, known as the Consulta Nacional de Rebeldías y Capturas (National Register of Fugiti…
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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