CEN/CLC/JTC 21 - Artificial Intelligence
The JTC shall produce standardization deliverables in the field of Artificial Intelligence (AI) and related use of data, as well as provide guidance to other technical committees concerned with Artificial Intelligence. The JTC shall also consider the adoption of relevant international standards and standards from other relevant organisations, like ISO/IEC JTC 1 and its subcommittees, such as SC 42 Artificial intelligence. The JTC shall produce standardization deliverables to address European market and societal needs and to underpin primarily EU legislation, policies, principles, and values.
Artificial Intelligence
The JTC shall produce standardization deliverables in the field of Artificial Intelligence (AI) and related use of data, as well as provide guidance to other technical committees concerned with Artificial Intelligence. The JTC shall also consider the adoption of relevant international standards and standards from other relevant organisations, like ISO/IEC JTC 1 and its subcommittees, such as SC 42 Artificial intelligence. The JTC shall produce standardization deliverables to address European market and societal needs and to underpin primarily EU legislation, policies, principles, and values.
General Information
- Draft15 pagesEnglish languagesale 10% offe-Library read for1 day
This document provides guidance on how organizations that develop, produce, deploy or use products, systems and services that utilize artificial intelligence (AI) can manage risk specifically related to AI. The guidance also aims to assist organizations to integrate risk management into their AI-related activities and functions. It moreover describes processes for the effective implementation and integration of AI risk management.
The application of this guidance can be customized to any organization and its context.
- Standard34 pagesEnglish languagesale 10% offe-Library read for1 day
- Draft31 pagesEnglish languagesale 10% offe-Library read for1 day
This document provides background about existing methods to assess the robustness of neural networks.
- Technical report39 pagesEnglish languagesale 10% offe-Library read for1 day
This document outlines a quality model for AI systems and is an application-specific extension to the standards on SQuaRE. The characteristics and sub-characteristics detailed in the model provide consistent terminology for specifying, measuring and evaluating AI system quality. The characteristics and sub-characteristics detailed in the model also provide a set of quality characteristics against which stated quality requirements can be compared for completeness.
- Draft20 pagesEnglish languagesale 10% offe-Library read for1 day
- Draft20 pagesEnglish languagesale 10% offe-Library read for1 day
This document defines a taxonomy of information elements to assist AI stakeholders with identifying and addressing the needs for transparency of AI systems. The document describes the semantics of the information elements and their relevance to the various objectives of different AI stakeholders.
This document uses a horizontal approach and is applicable to any kind of organization and application involving AI.
- Draft47 pagesEnglish languagesale 10% offe-Library read for1 day
This document provides mitigation techniques that can be applied throughout the AI system life
cycle in order to treat unwanted bias. This document describes how to address unwanted bias
in AI systems that use machine learning to conduct classification and regression tasks. This
document is applicable to all types and sizes of organization.
- Draft27 pagesEnglish languagesale 10% offe-Library read for1 day