Description: Evidence of the "filter-bubble effect" were found by vaccine-misinformation researchers in Amazon's recommendations, where its algorithms presented users who performed actions on misinformative products with more misinfomative products.
Entités
Voir toutes les entitésPrésumé : Un système d'IA développé et mis en œuvre par Amazon, endommagé Amazon Customers.
Statistiques d'incidents
ID
139
Nombre de rapports
2
Date de l'incident
2021-01-21
Editeurs
Sean McGregor, Khoa Lam
Applied Taxonomies
Classifications de taxonomie CSETv1
Détails de la taxonomieIncident Number
The number of the incident in the AI Incident Database.
139
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
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
Estimated Date
“Yes” if the data was estimated. “No” otherwise.
Yes
Classifications de taxonomie CSETv1_Annotator-1
Détails de la taxonomieIncident Number
The number of the incident in the AI Incident Database.
139
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.
No
Multiple AI Interaction
“Yes” if two or more independently operating AI systems were involved. “No” otherwise.
no
Classifications de taxonomie CSETv1_Annotator-2
Détails de la taxonomieIncident Number
The number of the incident in the AI Incident Database.
139
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
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
Estimated Date
“Yes” if the data was estimated. “No” otherwise.
No
Rapports d'incidents
Chronologie du rapport
arxiv.org · 2021
- Afficher le rapport d'origine à sa source
- Voir le rapport sur l'Archive d'Internet
Résumé : On craint de plus en plus que les plateformes de commerce électronique amplifient la désinformation sur les vaccins. Pour enquêter, nous menons deux séries d'audits algorithmiques pour la désinformation sur les vaccins sur les algo…
iol.co.za · 2021
- Afficher le rapport d'origine à sa source
- Voir le rapport sur l'Archive d'Internet
New York - Alors que l'on craint de plus en plus que les plateformes de commerce électronique amplifient la désinformation sur les vaccins, une nouvelle étude menée par des chercheurs de l'Université de Washington a révélé qu'Amazon héberge…
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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