Bias in Web Search Identified by Harvard Professor

A renowned Harvard professor has uncovered a potential issue with web search results, suggesting that certain topics may be prioritized over others without clear justification. This finding highlights the importance of responsible AI and trustworthy search algorithms to ensure fairness and accuracy in the information provided.

Matched TAIM controls

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

MEASURE 2.10 — similarity 0.468, rank 1. TAIM detail and related incidents →
MEASURE 2.11 — similarity 0.464, rank 2. TAIM detail and related incidents →
MAP 1.6 — similarity 0.439, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/19

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.