Description: Researchers from Boston University and Microsoft Research, New England demonstrated gender bias in the most common techniques used to embed words for natural language processing (NLP).
Entities
View all entitiesAlleged: Microsoft Research , Boston University and Google developed an AI system deployed by Microsoft Research and Boston University, which harmed Women and Minority Groups.
CSETv0 Taxonomy Classifications
Taxonomy DetailsPublic Sector Deployment
"Yes" if the AI system(s) involved in the accident were being used by the public sector or for the administration of public goods (for example, public transportation). "No" if the system(s) were being used in the private sector or for commercial purposes (for example, a ride-sharing company), on the other.
No
Lives Lost
Were human lives lost as a result of the incident?
No
Intent
Was the incident an accident, intentional, or is the intent unclear?
Unclear
Near Miss
Was harm caused, or was it a near miss?
Unclear/unknown
Ending Date
The date the incident ended.
2016-01-01T00:00:00.000Z
Beginning Date
The date the incident began.
2016-01-01T00:00:00.000Z
CSETv1 Taxonomy Classifications
Taxonomy DetailsIncident Number
The number of the incident in the AI Incident Database.
12
Incident Reports
Reports Timeline
arxiv.org · 2016
- View the original report at its source
- View the report at the Internet Archive
The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning…
Variants
A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.
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Similar Incidents
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