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Incidente 76: Live facial recognition is tracking kids suspected of being criminals

Descripción: Buenos Aires city government uses a facial recognition system that has led to numerous false arrests.

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Entidades

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Alleged: unknown developed an AI system deployed by Buenos Aires city government, which harmed Buenos Aires children.

Estadísticas de incidentes

ID
76
Cantidad de informes
1
Fecha del Incidente
2020-10-09
Editores
Sean McGregor, Khoa Lam
Applied Taxonomies
CSETv0, CSETv1, GMF, MIT

Clasificaciones de la Taxonomía CSETv0

Detalles de la Taxonomía

Problem 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

Clasificaciones de la Taxonomía CSETv1

Detalles de la Taxonomía

Incident Number

The number of the incident in the AI Incident Database.
 

76

Clasificaciones de la Taxonomía MIT

Machine-Classified
Detalles de la Taxonomía

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.
 
  1. Privacy & Security

Entity

Which, if any, entity is presented as the main cause of the risk
 

Human

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
 

Unintentional

Informes del Incidente

Cronología de Informes

+1
El reconocimiento facial en vivo rastrea a niños sospechosos de ser delincuentes
El reconocimiento facial en vivo rastrea a niños sospechosos de ser delincuentes

El reconocimiento facial en vivo rastrea a niños sospechosos de ser delincuentes

technologyreview.com

El reconocimiento facial en vivo rastrea a niños sospechosos de ser delincuentes
technologyreview.com · 2020
Traducido por IA

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 que comparte los mismos factores causales, produce daños similares e involucra los mismos sistemas inteligentes que un incidente de IA conocido. En lugar de indexar las variantes como incidentes completamente separados, enumeramos las variaciones de los incidentes bajo el primer incidente similar enviado a la base de datos. A diferencia de otros tipos de envío a la base de datos de incidentes, no se requiere que las variantes tengan informes como evidencia externa a la base de datos de incidentes. Obtenga más información del trabajo de investigación.

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