Descripción: Meta's AI chatbot in Facebook Messenger falsely accused multiple state lawmakers of sexual harassment, fabricating incidents, investigations, and consequences that never occurred. These fabricated stories, discovered by City & State, sparked outrage among the affected lawmakers and raised concerns about the reliability of the chatbot. Meta acknowledged the errors and committed to ongoing improvements.
Entidades
Ver todas las entidadesAlleged: Meta y Facebook users developed an AI system deployed by Meta, which harmed Meta , Facebook users , Kristen Gonzalez , Clyde Vanel y New York lawmakers.
Estadísticas de incidentes
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
3.1. False or misleading 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.
- Misinformation
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
If you're looking for information about state lawmakers, maybe don't trust Facebook's new Meta AI -- it may hallucinate about sexual harassment.
Facebook's chatbot, which launched in September as the latest in the trend of generative artif…
Meta's new chatbot invents sexual harassment allegations against US politicians. The allegations are fictitious, but the chatbot backs them up with a ton of details.
City & State obtained a screenshot of a Meta AI conversation in which the …
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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