Amazon’s Search and Recommendation Algorithms Found by Auditors to Have Boosted Products That Contained Vaccine Misinformation

January 21, 2021

Investigations uncovered the 'filter-bubble effect' in Amazon's search and recommendation algorithms, potentially promoting misleading vaccine information to users. This highlights the need for robust governance and responsible AI practices to prevent such incidents.

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Matched TAIM controls

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

MEASURE 2.6 — similarity 0.654, rank 1. TAIM detail and related incidents →
MEASURE 2.10 — similarity 0.644, rank 2. TAIM detail and related incidents →
MAP 1.6 — similarity 0.635, rank 3. TAIM detail and related incidents →

Alleged deployer: amazon
Alleged developer: amazon
Alleged harmed parties: amazon-customers

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/139

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.