Debiasing Bias in AI: Analyzing Word Embeddings for Gender Stereotypes — Mapping to the Govern Function of Project Cerebellum's Trusted AI Model
Explore our analysis of gender bias in word embeddings, a cornerstone of AI model training. Understand how this incident relates to the 'Govern' function within the HISPI Project Cerebellum Trusted AI Model (TAIM), emphasizing the importance of harm prevention and guardrails for AI. Ready to contribute? Join us in fostering trustworthy, safe, and secure AI JOIN US.
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/12
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.