Unveiling Bias in Automatically Derived Semantic Data: A Human-Like Phenomenon

A recent study reveals an alarming revelation - the semantics derived automatically from language corpora, which are often relied upon for AI applications, display human-like biases. This underscores the critical need for responsible AI governance and trustworthy AI models.

Matched TAIM controls

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

MAP 1.6 — similarity 0.593, rank 1. TAIM detail and related incidents →
MEASURE 2.5 — similarity 0.580, rank 2. TAIM detail and related incidents →
MAP 1.1 — similarity 0.578, 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.