Descripción: YouTube's recommendation algorithm has allegedly been directing teen users to harmful content promoting eating disorders and self-harm, according to a study by the Center for Countering Digital Hate. Almost 70% of the recommended videos in searches related to dieting or weight loss reportedly contained content likely to exacerbate body image anxieties.
Editor Notes: The full Center for Countering Digital Hate report is accessible at https://counterhate.com/wp-content/uploads/2024/12/CCDH.YoutubeED.Nov24.Report_FINAL.pdf.
Entidades
Ver todas las entidadesPresunto: un sistema de IA desarrollado e implementado por YouTube y Google, perjudicó a Adolescent girls y YouTube users.
Sistema de IA presuntamente implicado: YouTube
Estadísticas de incidentes
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
1.2. Exposure to toxic content
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
Anna Mockel tenía 14 años y de repente se obsesionó con perder peso. Era la primavera de 2020 y acababa de graduarse de octavo grado de forma remota. Confinada en casa y nerviosa por la transición a la escuela secundaria el próximo otoño, s…
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.
Incidentes Similares
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