Descripción: Eight years after Google Photos mislabeled images of Black individuals as "gorillas," image recognition software by Google, Apple, Amazon, and Microsoft still shows signs of either avoiding or inaccurately categorizing primates. Tests reveal that Google and Apple Photos refrain from labeling primates altogether, possibly to avoid the risk of perpetuating racial stereotypes. Microsoft OneDrive fails to identify any animals, while Amazon Photos overgeneralizes in its labeling.
Entidades
Ver todas las entidadesPresunto: un sistema de IA desarrollado e implementado por Google , Apple , Amazon y Microsoft, perjudicó a Consumers relying on accurate image categorization y members of racial and ethnic minorities who risk being stereotyped or misrepresented.
Estadísticas de incidentes
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
1.1. Unfair discrimination and misrepresentation
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.
- Discrimination and Toxicity
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
Unintentional
Informes del Incidente
Cronología de Informes
Ocho años después de una controversia sobre la etiqueta errónea de personas negras como gorilas mediante software de análisis de imágenes, y a pesar de los grandes avances en la visión por computadora, los gigantes tecnológicos todavía teme…
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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