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AI incident #1115 ·

Alleged Deepfake Investment Scam Uses Economist David Rosenberg's Likeness on Meta Platforms

Open on the AI Incident Database 2 news reports Synced from the AIID API · record last edited 7 Sep 2026

What happened

Fraudsters allegedly used AI-generated content and social media ads to impersonate economist David Rosenberg and promote a fraudulent investment scheme. Victims were reportedly directed to WhatsApp groups with fake stock tips, resulting in substantial financial losses, with some exceeding $500,000. The ads reportedly appeared on Meta platforms, including Facebook and Instagram. Rosenberg and his family reported the scam to authorities.

Editor's notes (AI Incident Database)

Timeline note: 04/15/2025 is an approximate date. The ads reportedly began circulating sometime in April 2025. This incident ID was created 06/22/2025.

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. Fraudulent Ads Posing as Renowned Economist David Rosenberg Offer Bogus Investment Schemes
    brokersview.com · BrokersView · AIID #5356
  2. Scammers are impersonating finance experts to steal millions – and the real ones are struggling to stop it
    theglobeandmail.com · Mariya Postelnyak · AIID #5357

Who was involved

Alleged harmed party
Unnamed woman who lost $500,000 Unnamed man who lost $450,000 Syed Hasan Investors General public Epistemic integrity David Rosenberg
On AIID: Unnamed woman who lost $500,000, Unnamed man who lost $450,000, Syed Hasan, Investors, General public, Epistemic integrity, David Rosenberg

AI systems implicated

WhatsAppSynthetic media generation technologySynthetic audio generation technologyMetaInstagramFacebookDeepfake technology

Classification (MIT AI Risk Repository taxonomy)

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

  • Misinformation and Manipulation

    "Recent studies have demonstrated that LLMs can be exploited to craft deceptive narratives with levels of persuasiveness similar to human-generated content (Pan et al., 2023b; Spitale et al., 2023), to fabri- cate fake n...

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

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

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