Description: Meta's open-source large language model, LLaMA, is allegedly being used to create graphic and explicit chatbots that indulge in violent and illegal sexual fantasies. The Washington Post highlighted the example of "Allie," a chatbot that participates in text-based role-playing allegedly involving violent scenarios like rape and abuse. The issue raises ethical questions about open-source AI models, their regulation, and the responsibility of developers and deployers in mitigating harmful usage.
Entidades
Ver todas las entidadesAlleged: Meta developed an AI system deployed by Individual developers or creators using Meta's LLaMA model, which harmed General public.
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
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
Sorpresa, sorpresa: la gente ya está usando el modelo de lenguaje grande (LLM) de Meta, LLaMA, una poderosa IA que Meta polémicamente [hizo de código abierto](https://futurism.com/the-byte/facebook-open-source-ai -pandoras-box) a principios…
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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