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Incident 1482: Purported AI-Generated Voice Reportedly Impersonated Washington Man's Daughter in $13,000 Extortion Scam

Description: A man in Washington state, Mark A. Young, reportedly wired $13,000 after scammers used a purportedly AI-generated copy of his daughter's voice to convince him she had been kidnapped after a staged car-crash story. The caller allegedly kept him on the phone for about 30 hours, directed him to banks and money-transfer locations, and sought another $17,000 before a Pullman bank manager and police confirmed his daughter was safe.
Editor Notes: Timeline notes: The incident date for this ID is 03/23/2026, when the extortion call reportedly began; the attempted additional withdrawal was reportedly interrupted at a Pullman bank on 03/24/2026; the Spokesman-Review report was published 05/08/2026 and updated 05/09/2026; the incident ID was created 5/10/2026.

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Entities

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Alleged: Deepfake technology developers and Synthetic audio generation technology developers developed an AI system deployed by Scammers, which harmed Mark A. Young , General public and Daughter of Mark A. Young.
Alleged implicated AI systems: Deepfake technology developers , Synthetic audio generation technology developers , Wire transfer services and Vishing infrastructure

Incident Stats

Incident ID
1482
Report Count
1
Incident Date
2026-03-23
Editors
Daniel Atherton
Applied Taxonomies
MIT

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

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
 

AI

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

Incident OccurrenceHis daughter called him crying, and then another voice got on the phone. Only one of them was real
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His daughter called him crying, and then another voice got on the phone. Only one of them was real

His daughter called him crying, and then another voice got on the phone. Only one of them was real

spokesman.com

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His daughter called him crying, and then another voice got on the phone. Only one of them was real
spokesman.com · 2026

Mark A. Young's daughter called him at his

home on a Monday. The young woman's panicked voice projected fear and pain.

She needed her father. "She was crying and upset," Young said. "She told me, 'Dad, I got in an accident and I'm in troubl…

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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