US CBP App's Failure to Detect Black Faces Reportedly Blocked Asylum Applications

January 18, 2023

Recent reports indicate a concerning incident involving CBP One's facial recognition feature, disproportionately failing to detect faces of Black asylum seekers from Haiti and African countries. This discriminatory bias in AI technology is a clear example of the need for trustworthy, responsible AI that prevents harm and promotes safety. The HISPI Project Cerebellum TAIM (Govern) seeks to establish guardrails for such AI practices, ensuring fairness, accountability, and transparency. JOIN US to learn more about how we're shaping the future of AI.

Matched TAIM controls

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

MAP 1.6 — similarity 0.677, rank 1. TAIM detail and related incidents →
MEASURE 2.6 — similarity 0.674, rank 2. TAIM detail and related incidents →
MAP 1.1 — similarity 0.672, rank 3. TAIM detail and related incidents →

Alleged deployer: us-customs-and-border-protection
Alleged developer: us-customs-and-border-protection
Alleged harmed parties: haitian-asylum-seekers, african-asylum-seekers, black-asylum-seekers

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/484

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.