HISPI Project Cerebellum HISPI PROJECT CEREBELLUM

Purported Deepfake Featuring Dr. Rinki Murphy and Jack Tame Reportedly Used to Promote Diabetes Scam in New Zealand

April 30, 2025

An alleged deepfake video, generated using artificial intelligence, reportedly impersonated Auckland University diabetes expert Dr. Rinki Murphy in a TVNZ interview with Jack Tame. The video falsely promoted a bogus diabetes cure, causing concern that it deceived New Zealanders into discontinuing prescribed medications. Despite reports, new variants of the misleading video have continued to surface on social media platforms from April to June 2025. This highlights the need for safe and secure AI practices in an era of increasing reliance on technology. For those interested in shaping responsible AI governance, learn more about how incidents like this map to the HISPI Project Cerebellum TAIM (Govern) or join us in preventing such harmful AI incidents: JOIN US.

Matched TAIM controls

Suggested mapping from embedding similarity (not a formal assessment). Browse all TAIM controls

Alleged deployer
unknown-scammers
Alleged developer
unknown-deepfake-technology-developers, unknown-voice-cloning-technology-developers
Alleged harmed parties
rinki-murphy, jack-tame, diabetes-patients, diabetes-patients-in-new-zealand, general-public-of-new-zealand

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/1102

Data source

Incident data is from the AI Incident Database (AIID).

When citing the database as a whole, please use:

McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.

Pre-print on arXiv · Database snapshots & citation guide

We use weekly snapshots of the AIID for stable reference. For the official suggested citation of a specific incident, use the “Cite this incident” link on each incident page.