AIPolicyTracker
Menu

AI incident #1397 ·

Deepfakes Reportedly Impersonated David Taylor-Robinson and Other UK Health Experts to Promote Wellness Nest Supplements

Open on the AI Incident Database 2 news reports Synced from the AIID API · record last edited 6 Sep 2026

What happened

In the UK, purported deepfake videos reportedly impersonated Professor David Taylor-Robinson and other health experts on TikTok and other platforms to promote supplements linked to Wellness Nest. The videos allegedly altered real footage and cloned voices to spread misleading medical claims about menopause, misuse identifiable individuals' likenesses, and expose viewers seeking health advice to health misinformation and deceptive marketing.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (2)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. AI deepfakes of real doctors spreading health misinformation on social media
    theguardian.com · Denis Campbell · AIID #6922
  2. AI Deepfakes of Real Doctors Spreading Health Misinformation on Social Media
    nphic.org · National Public Health Information Coalition (NPHIC) · AIID #7117

Who was involved

Alleged harmed party
Women seeking menopause advice Women and girls Women Victims of impersonation scams People seeking medical advice Epistemic integrity David Taylor-Robinson
On AIID: Women seeking menopause advice, Women and girls, Women, Victims of impersonation scams, People seeking medical advice, Epistemic integrity, David Taylor-Robinson

AI systems implicated

TikTokSynthetic media generation technologySynthetic audio generation technologyDeepfake technology

Classification (MIT AI Risk Repository taxonomy)

Causal entity
Human
Intent
Intentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 4.3.

  • Impersonation/identity theft

    "Impersonation/identity theft - Theft of an individual, group or organisation’s identity by a third-party in order to defraud, mock or otherwise harm them."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • IP/copyright loss

    "IP/copyright loss - Misuse or abuse of an individual or organisation’s intellectual property, including copyright, trademarks, and patents."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Dehumanisation/objectification

    "Dehumanisation/objectification - Use or misuse of a technology system to depict and/or treat people as not human, less than human, or as objects."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Defamation/libel/slander

    "Defamation/libel/slander - Use of a technology system to create, facilitate or amplify false perception(s) about an individual, group, or organisation."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Financial and business

    "Financial and Business - Use or misuse of a technology system in a manner that damages the financial interests of an individual or group, or which causes strategic, operational, legal or financial harm to a business or...

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cheating/plagiarism

    "Cheating/plagiarism - Use of another person’s or group’s words or ideas without consent and/or acknowledgement."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cybersecurity

    "LLMs may exacerbate cybersecurity risks in various ways (Newman, 2024). Firstly, LLMs may significantly amplify the effectiveness of deceptive operations aimed at tricking people into disclosing sensitive information or...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Domain-Specific Misuses

    "Improvements in LLMs may exert greater pressure to apply LLMs to various domains, such as health and education (Eloundou et al., 2023). Crude efforts to use LLMs in such domains, however, may incur harm and should be di...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

Linked by AIID editors or by its text-similarity model.

Incidents in the same risk subdomain

All incidents in this subdomain

Other incidents involving Synthetic media creators