Descripción: Investigative journalist Rana Ayyub was targeted by a deepfake porn campaign, where AI-generated explicit content falsely depicted her in a pornographic video. This was part of a broader effort to discredit and silence her, which included a doxxing attack that exposed her personal information that resulted in severe harassment and emotional distress.
Entidades
Ver todas las entidadesAlleged: Unknown deepfake technology developers developed an AI system deployed by Unknown deepfake creators, which harmed Rana Ayyub.
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
I get a lot of hate on social media. I'm an investigative journalist but I am also somebody who is seen as anti-establishment, and being a Muslim woman as well, I tick all the boxes.
The online world has always been a difficult place for me…
El veredicto ya estaba dado y era reconfortante: los deepfakes son el "perro que nunca ladró". Así lo afirmó Keir Giles, especialista en Rusia del Centro de Investigación de Estudios de Conflictos del Reino Unido. Giles razonó que la amenaz…
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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