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).
Entidades
Ver todas las entidadesPresunto: un sistema de IA desarrollado por Microsoft Research , Boston University y Google e implementado por Microsoft Research y Boston University, perjudicó a Women y Minority Groups.
Estadísticas de incidentes
ID
12
Cantidad de informes
1
Fecha del Incidente
2016-07-21
Editores
Sean McGregor
Applied Taxonomies
Clasificaciones de la Taxonomía CSETv0
Detalles de la TaxonomíaPublic 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
Clasificaciones de la Taxonomía CSETv1
Detalles de la TaxonomíaIncident Number
The number of the incident in the AI Incident Database.
12
Clasificaciones de la Taxonomía CSETv1_Annotator-1
Detalles de la TaxonomíaIncident Number
The number of the incident in the AI Incident Database.
12
Clasificaciones de la Taxonomía CSETv1_Annotator-3
Detalles de la TaxonomíaIncident 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
Informes del Incidente
Cronología de Informes
arxiv.org · 2016
- Ver el informe original en su fuente
- Ver el informe en el Archivo de Internet
La aplicación ciega del aprendizaje automático corre el riesgo de amplificar los sesgos presentes en los datos. Nos enfrentamos a un peligro de este tipo con la incrustación de palabras, un marco popular para representar datos de texto como…
Variantes
Una "Variante" es un incidente que comparte los mismos factores causales, produce daños similares e involucra los mismos sistemas inteligentes que un incidente de IA conocido. En lugar de indexar las variantes como incidentes completamente separados, enumeramos las variaciones de los incidentes bajo el primer incidente similar enviado a la base de datos. A diferencia de otros tipos de envío a la base de datos de incidentes, no se requiere que las variantes tengan informes como evidencia externa a la base de datos de incidentes. Obtenga más información del trabajo de investigación.
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