Google Image Results: Addressing Gender Bias in AI-Powered Searches

Recent research reveals that Google's image search algorithm may perpetuate gender biases, displaying predominantly male images when users seek information about CEO roles. This highlights the need for Responsible AI and safe and secure AI practices, ensuring harm prevention and trustworthy AI. The incident underscores the importance of guardrails for AI, including HISPI Project Cerebellum TAIM, to map, manage, and measure AI incidents. Engage with us through JOIN US to foster a more equitable future in AI.

Matched TAIM controls

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

GOVERN 3.1 — similarity 0.536, rank 1. TAIM detail and related incidents →
MEASURE 2.11 — similarity 0.523, rank 2. TAIM detail and related incidents →
MEASURE 1.3 — similarity 0.507, rank 3. TAIM detail and related incidents →

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.