Descripción: AI tools linked to China were used to disseminate disinformation targeting voters in the U.S. and Taiwan, according to a Microsoft report. These operations included AI-generated imagery and audio to influence political perceptions and election outcomes, originating from the APT Storm-1376 (also known as Spamouflage and Dragonbridge).
Entidades
Ver todas las entidadesPresunto: un sistema de IA desarrollado e implementado por Storm-1376 , Spamouflage , Dragonbridge y Chinese Communist Party, perjudicó a U.S. voters , Taiwanese voters , General public , Election integrity y Democracy.
Estadísticas de incidentes
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
4.1. Disinformation, surveillance, and influence at scale
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.
- Malicious Actors & Misuse
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
SAN FRANCISCO---Online actors linked to the Chinese government are increasingly leveraging artificial intelligence to target voters in the U.S., Taiwan and elsewhere with disinformation, according to new cybersecurity research and U.S. offi…
Los adversarios de Estados Unidos no afectaron significativamente los resultados de las elecciones estadounidenses de 2024, pero no por falta de intentos. Rusia, Irán y China llevaron a cabo agresivas operaciones de influencia dirigidas al …
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.