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Incidente 687: Deepfake Porn Sites Use Breeze Liu's Image Without Consent

Descripción: Porn sites are alleged to have used AI-generated images of Breeze Liu without her consent, leading to severe emotional distress. Liu discovered a video of herself on Pornhub, which was then deepfaked and spread across over 800 links. Despite efforts to remove the content, many sites refused to comply, perpetuating the violation and exploitation of her image.

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Alleged: Unknown deepfake creators developed an AI system deployed by Pornhub y Various porn sites, which harmed Breeze Liu.

Estadísticas de incidentes

ID
687
Cantidad de informes
1
Fecha del Incidente
2024-04-08
Editores
Daniel Atherton
Applied Taxonomies
MIT

Clasificaciones de la Taxonomía MIT

Machine-Classified
Detalles de la Taxonomía

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.
 
  1. 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

+1
Deepfake Porn Sites Use Victim's Image Without Consent
Deepfake Porn Sites Use Victim's Image Without Consent

Deepfake Porn Sites Use Victim's Image Without Consent

nytimes.com

Deepfake Porn Sites Use Victim's Image Without Consent
nytimes.com · 2024

This transcript was created using speech recognition software. While it has been reviewed by human transcribers, it may contain errors. Please review the episode audio before quoting from this transcript and email transcripts@nytimes.com wi…

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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