Incidente 651: Students at a Beverly Hills Middle School Allegedly Created and Shared Deepfake Nudes of Their Classmates

Descripción: At Beverly Vista Middle School in Beverly Hills, California, students allegedly used AI to generate fake nude photos with their classmates' faces, prompting investigations by school officials and the police. The incident highlights the increasing misuse of generative AI among minors.

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Alleged: Unknown deepfake creator developed an AI system deployed by , which harmed Unnamed middle school students.

Estadísticas de incidentes

ID
651
Cantidad de informes
3
Fecha del Incidente
2023-12-06
Editores
Daniel Atherton
Applied Taxonomies
MIT

Clasificaciones de la Taxonomía MIT

Machine-Classified
Detalles de la Taxonomía

Risk Subdomain

4.3. Fraud, scams, and targeted manipulation

Risk Domain

  1. Malicious Actors & Misuse

Entity

Human

Timing

Post-deployment

Intent

Intentional

A Beverly Hills middle school is investigating AI-made nude photos of students
nbcnews.com · 2024

Students at a middle school in Beverly Hills, California, used artificial intelligence technology to create fake nude photos of their classmates, according to school administrators. Now, the community is grappling with the fallout.

School o…

A.I. Is Making the Sexual Exploitation of Girls Even Worse
nytimes.com · 2024

On Tuesday, Kat Tenbarge and Liz Kreutz of NBC News reported that several middle schoolers in Beverly Hills, Calif., were caught making and distributing fake naked photos of their peers: "School officials at Beverly Vista Middle School were…

El auge de los deepfakes: adolescentes expuestos a pornografía generada por inteligencia artificial
highschool.latimes.com · 2024
Traducido por IA

Este informe tiene más detalles relacionados con el incidente 799, pero comienza haciendo referencia también a este incidente.

En febrero, el departamento de policía de Beverly Hills investigó a los estudiantes de la escuela secundaria Beve…

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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