Face ID Vulnerability Highlighted: A Call for Responsible AI Governance

Recent findings show that iPhone's Face ID can be manipulated, raising concerns about the safety and security of AI systems in everyday devices. While the potential exploit is significant, it underscores the need for robust AI governance, trustworthy AI practices, and harm prevention strategies. By understanding these incidents, we can implement guardrails for AI that promote safe and secure user experiences.

Matched TAIM controls

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

MAP 1.6 — similarity 0.558, rank 1. TAIM detail and related incidents →
MEASURE 2.10 — similarity 0.555, rank 2. TAIM detail and related incidents →
MEASURE 2.7 — similarity 0.535, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/26

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.