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Incidente 61: Overfit Kaggle Models Discouraged Data Science Competitors

Descripción: In the “The Nature Conservancy Fisheries Monitoring” competition on the data science competition website Kaggle, a number of competitors overfit their image classifier models to a poorly representative validation data set.

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Entidades

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Presunto: un sistema de IA desarrollado e implementado por Individual Kaggle Competitors, perjudicó a Individual Kaggle Competitors.

Estadísticas de incidentes

ID
61
Cantidad de informes
1
Fecha del Incidente
2017-05-01
Editores
Sean McGregor
Applied Taxonomies
CSETv0, CSETv1, GMF, MIT

Clasificaciones de la Taxonomía CSETv0

Detalles de la Taxonomía

Problem 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.
 

Software only

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.
 

Low

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.
 

Expert

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.
 

Images captured on fishing boats

Clasificaciones de la Taxonomía CSETv1

Detalles de la Taxonomía

Incident Number

The number of the incident in the AI Incident Database.
 

61

Clasificaciones de la Taxonomía MIT

Machine-Classified
Detalles de la Taxonomía

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

7.3. Lack of capability or robustness

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. AI system safety, failures, and limitations

Entity

Which, if any, entity is presented as the main cause of the risk
 

AI

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Unintentional

Informes del Incidente

Cronología de Informes

+1
Lo que aprendí de la competencia de pesca de Kaggle
Lo que aprendí de la competencia de pesca de Kaggle

Lo que aprendí de la competencia de pesca de Kaggle

medium.com

Lo que aprendí de la competencia de pesca de Kaggle
medium.com · 2017
Traducido por IA

Lo que aprendí de la competencia de pesca de Kaggle

Gidi Shperber Bloqueado Desbloquear Seguir Siguiendo 1 de mayo de 2017

TLDR:

Mi socio de Kaggle y yo participamos recientemente en la competencia de Kaggle "The Nature Conservancy Fisherie…

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.

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Investigación

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