Assessing the Impact of Incidents on AI Systems: Tesla's Robot Malfunction

Recent events at Tesla highlight the importance of safe and secure AI in high-stakes environments. A malfunctioning robot caused harm, underscoring the need for responsible AI governance to prevent such incidents. HISPI Project Cerebellum TAIM provides crucial insights into these cases, helping us learn and improve. Through JOIN US, you can join our community of contributors—a valuable resource in shaping a trustworthy future for AI.

Matched TAIM controls

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

MAP 4.2 — similarity 0.496, rank 1. TAIM detail and related incidents →
MAP 1.3 — similarity 0.482, rank 2. TAIM detail and related incidents →
MEASURE 2.6 — similarity 0.473, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/30

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.