AI's Struggle in Facial Recognition: Understanding the Challenges and Promoting Responsible Solutions

An exploration of the ongoing challenge faced by current algorithms in accurately recognizing Black faces, emphasizing the need for responsible AI governance and trustworthy AI models to ensure equitable and safe outcomes. By addressing these issues, we can contribute to the advancement of a secure and inclusive digital world—JOIN US.

Matched TAIM controls

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

MAP 1.6 — similarity 0.473, rank 1. TAIM detail and related incidents →
MEASURE 2.10 — similarity 0.465, rank 2. TAIM detail and related incidents →
MEASURE 2.5 — similarity 0.459, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/108

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.