Gender Bias in Word Embeddings: A Case Study on NLP
July 21, 2016
An investigation by researchers from Boston University and Microsoft Research, New England uncovered gender bias in popular word embedding techniques for Natural Language Processing (NLP). This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Ready to help shape trustworthy and responsible AI? JOIN US
- Alleged deployer
- microsoft-research, boston-university
- Alleged developer
- microsoft-research, google, boston-university
- Alleged harmed parties
- women, minority-groups
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
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