CEN/CLC/JTC 21 - Artificial Intelligence

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.

This document provides background about existing methods to assess the robustness of neural networks.

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.

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.

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.