Incidente 76: Según informes, el gobierno de Buenos Aires utilizó datos personales de niños en un sistema de reconocimiento facial para la búsqueda de fugitivos.
Descripción: A partir de abril de 2019, el gobierno de la ciudad de Buenos Aires supuestamente utilizó datos de la base de datos de fugitivos de la CONARC de Argentina, incluyendo identidades de menores y fotos de referencia, en su Sistema de Reconocimiento Facial para Fugitivos (SRFP). Human Rights Watch descubrió que al menos 166 menores habían aparecido en la CONARC entre 2017 y 2020 y advirtió que el sistema los exponía a violaciones de la privacidad y aumentaba el riesgo de coincidencias erróneas.
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.
Entidades
Ver todas las entidadesAlleged: Surveillance technology developers , NtechLab , Facial recognition system developers y Danaide S.A. developed an AI system deployed by Government of Argentina y Buenos Aires city government, which harmed Privacy , Minors , General public of Buenos Aires , General public of Argentina , General public , Buenos Aires children y Biometric data subjects.
Sistemas de IA presuntamente implicados: 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 y AI-enabled decision support systems
Clasificaciones de la Taxonomía CSETv1
Detalles de la TaxonomíaIncident Number
The number of the incident in the AI Incident Database.
76
Clasificaciones de la Taxonomía CSETv0
Detalles de la TaxonomíaProblem 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
Informes del Incidente
Cronología de Informes
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En una base de datos nacional en Argentina, decenas de miles de entradas detallan los nombres, cumpleaños y documentos de identidad de personas sospechosas de delitos. La base de datos, conocida como Consulta Nacional de Rebeldías y Captura…
Variantes
Una "Variante" es un incidente de IA similar a un caso conocido—tiene los mismos causantes, daños y sistema de IA. En lugar de enumerarlo por separado, lo agrupamos bajo el primer incidente informado. A diferencia de otros incidentes, las variantes no necesitan haber sido informadas fuera de la AIID. Obtenga más información del trabajo de investigación.
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