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CSETv0

¿Qué es la Taxonomía GMF?

La taxonomía de Objetivos, Métodos y Fallos (GMF, por sus siglas en inglés) es una taxonomía de análisis de causa de fallos que interrelaciona los objetivos de la implementación del sistema, los métodos del sistema y sus posibles fallos. Detalles sobre el proceso están disponibles en el trabajo reciente publicado para el paper SafeAI.

¿Cómo exploro la taxonomía?

Todas las taxonomías se pueden utilizar para filtrar informes de incidentes dentro de la Aplicación Discover. Los filtros de taxonomía funcionan de manera similar a cómo filtras productos en un sitio web de comercio electrónico. Usa el campo de búsqueda en la parte inferior de la pestaña “Clasificaciones” para encontrar el campo de taxonomía que te gustaría filtrar, luego haz clic en el valor deseado para aplicar el filtro.

Acerca de la Colaboración de IA Responsable

La Base de Datos de Incidentes de IA es un proyecto colaborativo de muchas personas y organizaciones. Los detalles sobre las personas y organizaciones que contribuyen a esta taxonomía en particular aparecerán aquí, mientras que puedes aprender más sobre el Colab en sí en las páginas de inicio de la base de datos de incidentes home y about.

Los encargados de mantener esta taxonomía incluyen,

  • Nikiforos Pittaras
  • Sean McGregor

Campos de Taxonomía

Overall severity of harm Buscable en la Aplicación Discover

Descubrir:
  • Negligible
    46 Incidentes
  • Minor
    19 Incidentes
  • Unclear/unknown
    16 Incidentes
  • Moderate
    12 Incidentes
  • Severe
    6 Incidentes

Definición: An estimate of the overall severity of harm caused. "Negligible" harm means minor inconvenience or expense, easily remedied. “Minor” harm means limited damage to property, social stability, the political system, or civil liberties occurred or nearly occurred. "Moderate" harm means that humans were injured (but not killed) or nearly injured, or that financial, property, social, or political interests or civil liberties were materially affected (or nearly so affected). "Severe" harm means that a small number of humans were or were almost gravely injured or killed, or that financial, property, social, or political interests or civil liberties were significantly disrupted at at least a regional or national scale (or nearly so disrupted). "Critical" harm means that many humans were or were almost killed, or that financial, property, social, or political interests were seriously disrupted at a national or global scale (or nearly so disrupted).

Uneven distribution of harms basis Buscable en la Aplicación Discover

Descubrir:
  • Race
    23 Incidentes
  • Sex
    13 Incidentes
  • Religion
    7 Incidentes
  • National origin or immigrant status
    6 Incidentes
  • Age
    5 Incidentes

Definición: If harms were unevenly distributed, this field indicates the basis or bases on which they were unevenly distributed.

Harm type Buscable en la Aplicación Discover

Descubrir:
  • Harm to social or political systems
    19 Incidentes
  • Psychological harm
    18 Incidentes
  • Harm to physical health/safety
    17 Incidentes
  • Harm to civil liberties
    16 Incidentes
  • Financial harm
    12 Incidentes

Definición: Indicates the type(s) of harm caused or nearly caused by the incident.

System developer Buscable en la Aplicación Discover

Descubrir:
  • Google
    18 Incidentes
  • Amazon
    6 Incidentes
  • Tesla
    4 Incidentes
  • Facebook
    4 Incidentes
  • Knightscope
    3 Incidentes

Definición: The entity that created the AI system.

Sector of deployment Buscable en la Aplicación Discover

Descubrir:
  • Información y comunicación
    26 Incidentes
  • Transporte y almacenamiento
    13 Incidentes
  • Artes, entretenimiento y recreación
    13 Incidentes
  • Administración pública y defensa
    12 Incidentes
  • Actividades administrativas y de servicios de apoyo
    7 Incidentes

Definición: The primary economic sector in which the AI system(s) involved in the incident were operating.

Relevant AI functions Buscable en la Aplicación Discover

Descubrir:
  • Cognition
    80 Incidentes
  • Perception
    66 Incidentes
  • Action
    57 Incidentes
  • Unclear
    7 Incidentes

Definición: Indicates whether the AI system(s) were intended to perform any of the following high-level functions: "Perception," i.e. sensing and understanding the environment; "Cognition," i.e. making decisions; or "Action," i.e. carrying out decisions through physical or digital means.

AI tools and techniques used Buscable en la Aplicación Discover

Descubrir:
  • machine learning
    19 Incidentes
  • Facial recognition
    6 Incidentes
  • open-source
    6 Incidentes
  • natural language processing
    5 Incidentes
  • environmental sensing
    5 Incidentes

Definición: Open-ended tags that indicate the hardware and software involved in the AI system(s).

AI functions and applications used Buscable en la Aplicación Discover

Descubrir:
  • decision support
    10 Incidentes
  • recommendation engine
    9 Incidentes
  • autonomous driving
    9 Incidentes
  • Facial recognition
    8 Incidentes
  • image recognition
    8 Incidentes

Definición: Open-ended tags that describe the functions and applications of the AI system.

Location Buscable en la Aplicación Discover

Descubrir:
  • Global
    27 Incidentes
  • United States
    6 Incidentes
  • Los Angeles, CA
    2 Incidentes
  • New Zealand
    2 Incidentes
  • Palo Alto, CA
    2 Incidentes
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Definición: The location or locations where the incident played out.

Named entities Buscable en la Aplicación Discover

Descubrir:
  • Google
    18 Incidentes
  • Amazon
    8 Incidentes
  • Microsoft
    6 Incidentes
  • Tesla
    4 Incidentes
  • Facebook
    4 Incidentes

Definición: All named entities (such as people, organizations, locations, and products - generally proper nouns) that seem to have a significant relationship with this event, as indicated by the available evidence.

Party responsible for AI system Buscable en la Aplicación Discover

Descubrir:
  • Google
    19 Incidentes
  • Amazon
    7 Incidentes
  • Tesla
    5 Incidentes
  • Facebook
    4 Incidentes
  • Apple
    4 Incidentes

Definición: A list of parties (up to three) that were responsible for the relevant AI tool or system, i.e. that had operational control over the AI-related system causing harm (or control over those who did).

Harm nearly missed? Buscable en la Aplicación Discover

Descubrir:
  • Unclear/unknown
    44 Incidentes
  • Harm caused
    41 Incidentes
  • Near miss
    15 Incidentes

Definición: Was harm caused, or was it a near miss?

Probable level of intent Buscable en la Aplicación Discover

Descubrir:
  • Accident
    72 Incidentes
  • Unclear
    24 Incidentes
  • Deliberate or expected
    4 Incidentes

Definición: Indicates whether the incident was deliberate/expected or accidental, based on the available evidence. "Deliberate or expected" applies if it is established or highly likely that the system acted more or less as expected, from the perspective of at least one of the people or entities responsible for it. “Accident” applies if it is established or highly likely that the harm arose from the system acting in an unexpected way. "Unclear" applies if the evidence is contradictory or too thin to apply either of the above labels.

Human lives lost Buscable en la Aplicación Discover

Descubrir:
  • false
    92 Incidentes
  • true
    8 Incidentes

Definición: Marked "trur" if one or more people died as a result of the accident, "false" if there is no evidence of lives being lost, "unclear" otherwise.

Critical infrastructure sectors affected Buscable en la Aplicación Discover

Descubrir:
  • Transportation
    10 Incidentes
  • Healthcare and public health
    4 Incidentes
  • Communications
    2 Incidentes
  • Government facilities
    2 Incidentes
  • Financial services
    1 Incidente

Definición: Where applicable, this field indicates if the incident caused harm to any of the economic sectors designated by the U.S. government as critical infrastructure.

Public sector deployment Buscable en la Aplicación Discover

Descubrir:
  • false
    88 Incidentes
  • true
    12 Incidentes

Definición: "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.

Nature of end user Buscable en la Aplicación Discover

Descubrir:
  • Amateur
    73 Incidentes
  • Expert
    18 Incidentes

Definición: "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.

Level of autonomy Buscable en la Aplicación Discover

Descubrir:
  • Medium
    36 Incidentes
  • High
    31 Incidentes
  • Low
    14 Incidentes
  • Unclear/unknown
    10 Incidentes

Definición: 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.

Physical system Buscable en la Aplicación Discover

Descubrir:
  • Software only
    66 Incidentes
  • Vehicle/mobile robot
    16 Incidentes
  • Consumer device
    7 Incidentes
  • Unknown/unclear
    2 Incidentes
  • Other:Medical system
    1 Incidente

Definición: Where relevant, indicates whether the AI system(s) was embedded into or tightly associated with specific types of hardware.

Causative factors within AI system Buscable en la Aplicación Discover

Descubrir:
  • Specification
    45 Incidentes
  • Robustness
    35 Incidentes
  • Unknown/unclear
    22 Incidentes
  • Assurance
    16 Incidentes

Definición: 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.

Full description of the incident

Definición: A plain-language description of the incident in one paragraph or less.

Short description of the incident

Definición: A one-sentence description of the incident.

Description of AI system involved

Definición: A brief description of the AI system(s) involved in the incident, including the system’s intended function, the context in which it was deployed, and any available details about the algorithms, hardware, and training data involved in the system.

Beginning date

Definición: The date the incident began.

Ending date

Definición: The date the incident ended.

Total financial cost

Definición: The stated or estimated financial cost of the incident, if reported.

Laws covering the incident

Definición: Relevant laws under which entities involved in the incident may face legal liability as a result of the incident.

Description of the data inputs to the AI systems

Definición: A brief description of the data that the AI system(s) used or were trained on.

Investigación

  • Definición de un “Incidente de IA”
  • Definición de una “Respuesta a incidentes de IA”
  • Hoja de ruta de la base de datos
  • Trabajo relacionado
  • Descargar Base de Datos Completa

Proyecto y Comunidad

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  • Contactar y Seguir
  • Aplicaciones y resúmenes
  • Guía del editor

Incidencias

  • Todos los incidentes en forma de lista
  • Incidentes marcados
  • Cola de envío
  • Vista de clasificaciones
  • Taxonomías

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