Incident 76: Le gouvernement de Buenos Aires aurait utilisé les données personnelles d'enfants dans un système de reconnaissance faciale pour traquer les fugitifs.
Description: À partir d'avril 2019, la municipalité de Buenos Aires aurait utilisé des données de la base de données argentine des fugitifs CONARC, notamment l'identité d'enfants et des photos de référence, dans son système de reconnaissance faciale en temps réel pour les fugitifs (SRFP). Human Rights Watch a constaté qu'au moins 166 enfants figuraient dans la base de données CONARC entre 2017 et 2020 et a alerté sur le fait que ce système les exposait à des violations de leur vie privée et à un risque accru d'erreurs d'identification.
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.
Entités
Voir toutes les entitésAlleged: Surveillance technology developers , NtechLab , Facial recognition system developers et Danaide S.A. developed an AI system deployed by Government of Argentina et Buenos Aires city government, which harmed Privacy , Minors , General public of Buenos Aires , General public of Argentina , General public , Buenos Aires children et Biometric data subjects.
Systèmes d'IA présumés impliqués: 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 et AI-enabled decision support systems
Classifications de taxonomie CSETv0
Détails de la taxonomieProblem 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
Classifications de taxonomie CSETv1
Détails de la taxonomieIncident Number
The number of the incident in the AI Incident Database.
76
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
Rapports d'incidents
Chronologie du rapport
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Dans une base de données nationale en Argentine, des dizaines de milliers d'entrées détaillent les noms, les anniversaires et les cartes d'identité nationales des personnes soupçonnées de crimes. La base de données, connue sous le nom de Co…
Variantes
Une "Variante" est un incident de l'IA similaire à un cas connu—il a les mêmes causes, les mêmes dommages et le même système intelligent. Plutôt que de l'énumérer séparément, nous l'incluons sous le premier incident signalé. Contrairement aux autres incidents, les variantes n'ont pas besoin d'avoir été signalées en dehors de la base de données des incidents. En savoir plus sur le document de recherche.
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