Description: A Channel 4 News investigation alleges that nearly 4,000 celebrities globally, including 255 British figures, were victims of deepfake pornography. Faces were superimposed onto explicit content using AI, with the top deepfake sites garnering 100 million views in three months, according to their findings.
Entities
View all entitiesAlleged: Unknown deepfake technology developers developed an AI system deployed by Deepfake website operators, which harmed celebrities , British public figures and Cathy Newman.
Incident Stats
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
Incident Reports
Reports Timeline
More than 250 British celebrities are among the thousands of famous people who are victims of deepfake pornography, an investigation has found.
A Channel 4 News analysis of the five most visited deepfake websites found almost 4,000 famous i…
Variants
A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.
