Descripción: Scammers are using AI to impersonate small businesses by copying their videos, logos, and social media posts. They create fake listings and ads, diverting customers to cheap knockoffs or stealing their money. This has severely impacted businesses like Bee Cups, Darn Tough Vermont, and Cascade hummingbird feeders, leading to significant financial losses, negative reviews, and damaged reputations. Their deployment of AI makes it challenging for small businesses to combat these fraudulent activities.
Entidades
Ver todas las entidadesAlleged: OpenAI y Unknown AI developers developed an AI system deployed by Unknown scammers, which harmed small businesses , Small business customers , Small business employees , Bee Cups , Darn Tough Vermont y Jim Carter.
Estadísticas de incidentes
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
4.3. Fraud, scams, and targeted manipulation
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
Copycats are stepping up their attacks on small businesses.
Sellers of products including merino socks and hummingbird feeders say they have lost customers to online scammers who use the legitimate business owners' videos, logos and social…
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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