Exploring Bias in AI Image Search: A Closer Look at Google's Algorithm

Recent social media posts claim that searching 'Three Black Teenagers' on Google yields different results compared to 'Three White Teenagers'. Is this statement true? Let's delve into the intricacies of AI image search algorithms and uncover the underlying truth. The issue at hand sheds light on potential biases in AI, a topic crucial for responsible AI governance and harm prevention in our increasingly digital world.

Matched TAIM controls

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

GOVERN 3.1 — similarity 0.486, rank 1. TAIM detail and related incidents →
MAP 1.6 — similarity 0.475, rank 2. TAIM detail and related incidents →
MAP 3.1 — similarity 0.466, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/53

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.