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.
Incident Stats
Incident ID
12
Report Count
1
Incident Date
2016-07-21
Editors
Sean McGregor
Applied Taxonomies
CSETv1 Taxonomy Classifications
Taxonomy DetailsIncident Number
The number of the incident in the AI Incident Database.
12
CSETv1_Annotator-1 Taxonomy Classifications
Taxonomy DetailsIncident Number
The number of the incident in the AI Incident Database.
12
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_Annotator-3 Taxonomy Classifications
Taxonomy DetailsIncident Number
The number of the incident in the AI Incident Database.
12
Notes (special interest intangible harm)
Input any notes that may help explain your answers.
gender bias
Special Interest Intangible Harm
An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.
yes
Date of Incident Year
The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.
Enter in the format of YYYY
2016
Date of Incident Month
The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.
Enter in the format of MM
7
Date of Incident Day
The day on which the incident occurred. If a precise date is unavailable, leave blank.
Enter in the format of DD
21
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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