Preventing AI Incidents: The Consequences of Failing Algorithms

Understanding the potential risks and impacts of faulty algorithms in AI systems is crucial for responsible AI governance. Learn from real-world incidents, their causes, and consequences, to develop safeguards and ensure safe and secure AI deployments. Join HISPI Project Cerebellum TAIM contributors—JOIN US—to learn more about harm prevention in AI.

Matched TAIM controls

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

MEASURE 2.6 — similarity 0.582, rank 1. TAIM detail and related incidents →
MAP 3.2 — similarity 0.579, rank 2. TAIM detail and related incidents →
MEASURE 1.3 — similarity 0.564, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/49

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.