Amazon's Rekognition Falsely Matched Members of Congress to Mugshots

July 26, 2018

The ACLU exposed a concerning incident where Amazon's Rekognition face comparison feature falsely matched members of Congress, particularly those of color, to mugshots. This underscores the importance of safe and secure AI practices and the need for effective governance through initiatives like HISPI Project Cerebellum TAIM (Govern) to prevent such harm.

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Matched TAIM controls

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

GOVERN 1.2 — similarity 0.636, rank 1. TAIM detail and related incidents →
MEASURE 2.10 — similarity 0.636, rank 2. TAIM detail and related incidents →
MAP 1.6 — similarity 0.631, rank 3. TAIM detail and related incidents →

Alleged deployer: amazon
Alleged developer: amazon
Alleged harmed parties: rekognition-users, arrested-people

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/114

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.