AI incident #1105 ·

Michigan Woman Defrauded in Alleged Tinder Romance Scam Using Purportedly AI-Generated Video Calls

What happened

A Michigan woman, Beth Hyland, was reportedly defrauded of $26,000 in a romance scam conducted over Tinder, in which the perpetrator, "Richard," used purportedly AI-generated video technology during Skype calls to build trust. The scammer, allegedly part of Nigerian Yahoo Boys networks, reportedly posed as a French construction manager and claimed to need funds for legal and translation services. Hyland reportedly sent funds over several months. She later spoke at a U.S. Senate hearing on dating

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 (7)

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

  1. Victims of 'romance scams' turn to Congress for help
    nbcnews.com · Kate Santaliz, Julie Tsirkin · AIID #5333
  2. Deepfake romance scam leaves woman $26,000 in debt
    khaleejtimes.com · Reuters, Khaleej Times · AIID #5332
  3. Tinder for a firestorm: deepfake video quality fuels romance baiting scams
    timeslive.co.za · Kim Harrisberg, Adam Smith, Thomson Reuters Foundation · AIID #5335
  4. Deep love or deepfake: Dating in the time of AI
    japantimes.co.jp · Thomson Reuters Foundation, The Japan Times · AIID #5336

Who was involved

Alleged harmed party
Tinder, Beth Hyland

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)

Incidents in the same risk subdomain

All incidents in this subdomain