Description: Guy's and St Thomas' NHS Foundation Trust warned that purported AI-generated videos circulated on Facebook and TikTok depicted its clinicians endorsing weight loss patches. The videos allegedly impersonated doctors and used misleading medical claims to market a product.
Entities
View all entitiesAlleged: Deepfake technology developers and Synthetic audio generation technology developers developed an AI system deployed by Scammers, which harmed People seeking medical advice , Guy's and St Thomas' NHS Foundation Trust clinicians , Guy's and St Thomas' NHS Foundation Trust , General public of the United Kingdom , General public and Epistemic integrity.
Alleged implicated AI systems: TikTok , Social media platforms , Facebook , Deepfake technology and Synthetic audio generation technology
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
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A hospital trust in south London has issued an alert after fraudulent videos were circulated online claiming its staff endorsed weight loss products.
Guy's and St Thomas' NHS Foundation Trust said that the videos, found on social media plat…
Variants
A "variant" is an AI incident similar to a known case—it has the same causes, harms, and AI system. Instead of listing it separately, we group it under the first reported incident. Unlike other incidents, variants do not need to have been reported outside the AIID. Learn more from the research paper.
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