Description: Throughout 2024, schools in Australia dealt with a significant rise and proliferation of non-consensual deepfake pornography of students. Often, male students are reported to use "nudify" apps such as Undress AI with images of their classmates and teachers. Many of the sites have remained legal and accessible to minors, who in turn are using the sites to generate pornography of their peers.
Entidades
Ver todas las entidadesAlleged: Undress AI y Unknown deepfake technology developers developed an AI system deployed by Australian students y Unknown deepfake creators, which harmed Australian students y Australian children.
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
"Vea a cualquier persona desnuda gratis", dice el lema del sitio web.
"Simplemente pinte sobre la ropa, indique la edad y el tipo de cuerpo y obtenga un desnudo profundo en unos segundos".
Más de 100.000 personas utilizan el sitio web "Undr…
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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