Description: A self-driving public shuttle by Keolis North America and Navya was involved in a collision with a human-driven delivery truck in Las Vegas, Nevada on its first day of service.
Entidades
Ver todas las entidadesPresunto: un sistema de IA desarrollado e implementado por Navya y Keolis North America, perjudicó a Navya , Keolis North America y bus passengers.
Estadísticas de incidentes
ID
23
Cantidad de informes
24
Fecha del Incidente
2017-11-08
Editores
Sean McGregor, Khoa Lam
Applied Taxonomies
Clasificaciones de la Taxonomía CSETv1
Detalles de la TaxonomíaIncident Number
The number of the incident in the AI Incident Database.
23
Clasificaciones de la Taxonomía CSETv1_Annotator-1
Detalles de la TaxonomíaIncident Number
The number of the incident in the AI Incident Database.
23
Clasificaciones de la Taxonomía CSETv1_Annotator-2
Detalles de la TaxonomíaIncident Number
The number of the incident in the AI Incident Database.
23
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.
no
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
2017
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
11
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
8
Estimated Date
“Yes” if the data was estimated. “No” otherwise.
Yes
Clasificaciones de la Taxonomía CSETv0
Detalles de la TaxonomíaProblem Nature
Indicates which, if any, of the following types of AI failure describe the incident: "Specification," i.e. the system's behavior did not align with the true intentions of its designer, operator, etc; "Robustness," i.e. the system operated unsafely because of features or changes in its environment, or in the inputs the system received; "Assurance," i.e. the system could not be adequately monitored or controlled during operation.
Robustness
Physical System
Where relevant, indicates whether the AI system(s) was embedded into or tightly associated with specific types of hardware.
Vehicle/mobile robot
Level of Autonomy
The degree to which the AI system(s) functions independently from human intervention. "High" means there is no human involved in the system action execution; "Medium" means the system generates a decision and a human oversees the resulting action; "low" means the system generates decision-support output and a human makes a decision and executes an action.
High
Nature of End User
"Expert" if users with special training or technical expertise were the ones meant to benefit from the AI system(s)’ operation; "Amateur" if the AI systems were primarily meant to benefit the general public or untrained users.
Amateur
Public 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
Data Inputs
A brief description of the data that the AI system(s) used or were trained on.
Traffic patterns, data from cameras used to perceive the surroundings
Clasificaciones de la Taxonomía CSETv1_Annotator-3
Detalles de la TaxonomíaIncident Number
The number of the incident in the AI Incident Database.
23