Description: Unsupervised image generation models trained using Internet images such as iGPT and SimCLR were shown to have embedded racial, gender, and intersectional biases, resulting in stereotypical depictions.
Entités
Voir toutes les entitésPrésumé : Un système d'IA développé et mis en œuvre par OpenAI and Google, endommagé gender minority groups , racial minority groups and underrepresented groups in training data.
Statistiques d'incidents
ID
367
Nombre de rapports
1
Date de l'incident
2020-06-17
Editeurs
Khoa Lam
Applied Taxonomies
Classifications de taxonomie CSETv1
Détails de la taxonomieIncident Number
The number of the incident in the AI Incident Database.
367
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
2021
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
01
Estimated Date
“Yes” if the data was estimated. “No” otherwise.
Yes
Multiple AI Interaction
“Yes” if two or more independently operating AI systems were involved. “No” otherwise.
no
Classifications de taxonomie CSETv1_Annotator-1
Détails de la taxonomieIncident Number
The number of the incident in the AI Incident Database.
367
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
2021
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
01
Estimated Date
“Yes” if the data was estimated. “No” otherwise.
Yes
Multiple AI Interaction
“Yes” if two or more independently operating AI systems were involved. “No” otherwise.
no
Rapports d'incidents
Chronologie du rapport
technologyreview.com · 2021
- Afficher le rapport d'origine à sa source
- Voir le rapport sur l'Archive d'Internet
Ryan Steed, doctorant à l'université Carnegie Mellon, et Aylin Caliskan, professeur adjoint à l'université George Washington, ont examiné deux algorithmes : [iGPT d'OpenAI](https://www.technologyreview.com/2020/07/16/1005284 /openai-ai-gpt-…
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.
