Unveiling Racial and Gender Biases in AI: A Crucial Examination

A groundbreaking study has shed light on the alarming issue of racial and gender biases within AI systems. The research, conducted by renowned institutions, aims to foster responsible AI development and governance. By addressing these biases, we can ensure safe and secure AI deployment for all.

Matched TAIM controls

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

GOVERN 3.1 — similarity 0.631, rank 1. TAIM detail and related incidents →
MAP 3.1 — similarity 0.629, rank 2. TAIM detail and related incidents →
MAP 1.2 — similarity 0.626, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/59

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.