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).
Entités
Voir toutes les entitésPrésumé : un système d'IA développé par Microsoft Research , Boston University and Google et mis en œuvre par Microsoft Research and Boston University, endommagé Women and Minority Groups.
Statistiques d'incidents
ID
12
Nombre de rapports
1
Date de l'incident
2016-07-21
Editeurs
Sean McGregor
Applied Taxonomies
Classifications de taxonomie CSETv0
Détails de la taxonomiePublic 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
Classifications de taxonomie CSETv1
Détails de la taxonomieIncident Number
The number of the incident in the AI Incident Database.
12
Classifications de taxonomie CSETv1_Annotator-1
Détails de la taxonomieIncident Number
The number of the incident in the AI Incident Database.
12
Classifications de taxonomie CSETv1_Annotator-3
Détails de la taxonomieIncident 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
Rapports d'incidents
Chronologie du rapport
arxiv.org · 2016
- Afficher le rapport d'origine à sa source
- Voir le rapport sur l'Archive d'Internet
L'application aveugle de l'apprentissage automatique risque d'amplifier les biais présents dans les données. Nous sommes confrontés à un tel danger avec l'incorporation de mots, un cadre populaire pour représenter les données textuelles sou…
Variantes
Une "Variante" est un incident qui partage les mêmes facteurs de causalité, produit des dommages similaires et implique les mêmes systèmes intelligents qu'un incident d'IA connu. Plutôt que d'indexer les variantes comme des incidents entièrement distincts, nous listons les variations d'incidents sous le premier incident similaire soumis à la base de données. Contrairement aux autres types de soumission à la base de données des incidents, les variantes ne sont pas tenues d'avoir des rapports en preuve externes à la base de données des incidents. En savoir plus sur le document de recherche.
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