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Incidente 109: PimEyes's Facial Recognition AI Allegedly Lacked Safeguards to Prevent Itself from Being Abused

Descripción: PimEyes offered its subscription-based AI service to anyone in the public to search for matching facial images across the internet, which critics said lacked public oversight and government rules to prevent itself from misuse such as stalking women.

Herramientas

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Entidades

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Presunto: un sistema de IA desarrollado e implementado por PimEyes, perjudicó a internet users.

Estadísticas de incidentes

ID
109
Cantidad de informes
1
Fecha del Incidente
2017-01-01
Editores
Sean McGregor, Khoa Lam
Applied Taxonomies
CSETv1, GMF, MIT

Clasificaciones de la Taxonomía CSETv1

Detalles de la Taxonomía

Incident Number

The number of the incident in the AI Incident Database.
 

109

AI Tangible Harm Level Notes

Notes about the AI tangible harm level assessment
 

The article notes the potential for nefarious actors like stalkers to use the PimEyes tool to do tangible harm.

Special Interest Intangible Harm

An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.
 

maybe

Notes (AI special interest intangible harm)

If for 5.5 you select unclear or leave it blank, please provide a brief description of why. You can also add notes if you want to provide justification for a level.
 

Annotator 2:

It is unclear whether PimEyes violates peoples' privacy and whether or not that constitutes a rights violation.

Date of Incident Year

The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank. Enter in the format of YYYY
 

2021

Date of Incident Month

The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank. Enter in the format of MM
 

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
 

Intentional

Informes del Incidente

Cronología de Informes

Incident OccurrenceEste sitio web de reconocimiento facial puede convertir a cualquiera en policía o acosador
Este sitio web de reconocimiento facial puede convertir a cualquiera en policía o acosador

Este sitio web de reconocimiento facial puede convertir a cualquiera en policía o acosador

washingtonpost.com

Este sitio web de reconocimiento facial puede convertir a cualquiera en policía o acosador
washingtonpost.com · 2021
Traducido por IA

PimEyes se ha convertido en un éxito entre los "creeps" digitales y otros ansiosos por investigar a los extraños. Los investigadores temen que no haya forma de evitar que se abuse de él. El sitio de reconocimiento facial PimEyes es una de l…

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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