CEN/CLC/JTC 21 - Artificial Intelligence

This document describes how to address unwanted bias in AI systems that use machine learning to conduct classification and regression tasks. This document provides mitigation techniques that can be applied throughout the AI system life cycle in order to treat unwanted bias. This document is applicable to all types and sizes of organization.

This document provides an overview on AI-related standards, with a focus on data and data life cycles, to organizations, agencies, enterprises, developers, universities, researchers, focus groups, users, and other stakeholders that are experiencing this era of digital transformation.
It describes links among the many international standards and regulations published or under development, with the aim of promoting a common language, a greater culture of quality, giving an information framework.
It addresses the following areas:
-   data governance;
-   data quality;
-   elements for data, data sets properties to provide unbiased evaluation and information for testing.

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 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 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.

he proposed document will establish a framework for quantification of environmental impact of AI and its long-term sustainability, and
encourage AI developers and users to improve efficiency of AI use. It will also provide a summary of the state of the art of AI technology for direct control and optimisation of energy use in energy systems. The document will provide life-cycle assessment of AI development, deployment and use.
Emissions that are produced directly by combustion of fossil fuels are Scope 1 emissions. These are observed in transport system
and in fossil-fuel energy generators, and the like. AI may help reduce Scope 1 emissions via smart interventions (demand-side response, optimisation of combustion, etc.) Scope 2 are indirect emissions from electricity use, and AI will play a major role in reducing these emissions. Scope 3 are emissions produced during a life cycle of a technology – these emissions are important in assessment of AI solution and will be in scope of this project. Emissions of Scope 4 are the avoided emissions – AI has great potential in quantifying avoided emissions (carbon savings), and the report will address this as well.

This document sets out a review of the current methods and practices (including tools, assets, and conditions of acceptability) for
conformity assessment in respect to, among others, products, services, processes, management systems, organizations, or persons,
as relevant for the development and use of AI systems. It includes an industry horizontal (vertical agnostic) perspective as well as an
industry vertical perspective.
This document focuses only on the process of assessment and gap analysis of conformity. It defines the objects of conformity
related to AI systems and all other related aspects of the process of conformity assessment. The document also reviews to what
extent AI poses specific challenges with respect to assessment of, for example, software engineering, data quality and engineering
processes.
This document takes into account requirements and orientations from policy frameworks such as the EU AI strategy and those from
CEN and CENELEC member countries.
This document is intended for technologists, standards bodies, regulators and interested parties.