Description: SN Technologies allegedly misled Lockport City Schools about the performance of its AEGIS face and weapons detection systems, downplaying error rates for Black faces and weapon misidentification.
Entidades
Ver todas las entidadesPresunto: un sistema de IA desarrollado por SN Technologies e implementado por Lockport City School District, perjudicó a Black students.
Estadísticas de incidentes
ID
214
Cantidad de informes
1
Fecha del Incidente
2020-01-02
Editores
Khoa Lam
Applied Taxonomies
Clasificaciones de la Taxonomía CSETv1
Detalles de la TaxonomíaIncident Number
The number of the incident in the AI Incident Database.
214
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
2020
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
Estimated Date
“Yes” if the data was estimated. “No” otherwise.
No
Clasificaciones de la Taxonomía CSETv1_Annotator-1
Detalles de la TaxonomíaIncident Number
The number of the incident in the AI Incident Database.
214
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
2020
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
Estimated Date
“Yes” if the data was estimated. “No” otherwise.
No
Informes del Incidente
Cronología de Informes
vice.com · 2020
- Ver el informe original en su fuente
- Ver el informe en el Archivo de Internet
Los documentos revelan que la tecnología de reconocimiento facial de Lockport Schools ha confundido los mangos de las escobas con armas de fuego y ha identificado erróneamente a los estudiantes negros a tasas mucho más altas.
Desde que supi…
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.