The future of fake news: don't believe everything you read, see or hear

In an era where artificial intelligence powers media platforms, the line between fact and fiction can blur significantly. This article explores the challenges and potential solutions for responsible AI governance to combat the rising tide of misinformation.

Matched TAIM controls

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

GOVERN 6.2 — similarity 0.501, rank 1. TAIM detail and related incidents →
MAP 1.6 — similarity 0.493, rank 2. TAIM detail and related incidents →
GOVERN 1.7 — similarity 0.490, rank 3. TAIM detail and related incidents →

AI governance case studies

For forensic AI governance failure analysis (TAIMScore™ case studies), browse Human Signal’s Failure Files™.

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/39

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.