Over 400 Purportedly AI-Driven Scams Reportedly Led to $8M Loss for Australians in 2023
March 1, 2024
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Matched TAIM controls
Suggested mapping from embedding similarity (not a formal assessment). Browse all TAIM controls
- MAP 3.2 — similarity 0.629, rank 1. TAIM detail and related incidents →
- MEASURE 2.6 — similarity 0.619, rank 2. TAIM detail and related incidents →
- MAP 4.1 — similarity 0.617, rank 3. TAIM detail and related incidents →
- Alleged deployer
- unknown-scammers
- Alleged developer
- unknown-voice-cloning-technology-developers, unknown-generative-ai-developers, unknown-deepfake-technology-developers
- Alleged harmed parties
- general-public-of-australia, general-public, epistemic-integrity
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/715
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.