The Hidden Gender Bias In Google Image Search

This article explores a recent incident where gender bias was detected in Google's Image Search results. The bias, though seemingly subtle, highlights the need for robust AI governance and trustworthy AI practices. By mapping this issue to HISPI Project Cerebellum TAIM (Measure), we can strive towards safer and more secure AI applications.

Matched TAIM controls

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

MEASURE 2.10 — similarity 0.536, rank 1. TAIM detail and related incidents →
MEASURE 2.11 — similarity 0.536, rank 2. TAIM detail and related incidents →
MAP 1.6 — similarity 0.508, rank 3. TAIM detail and related incidents →

AI governance case studies

For forensic AI governance failure analysis (TAIMScore™ case studies), browse Human Signal’s Failure Files™.

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/18

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.