CEN/CENELEC

This document specifies the requirements and provides guidance for the definition, implementation and maintenance of a quality management system for organizations that provide AI systems. This document is intended to support the organization in meeting applicable regulatory requirements. It is primarily intended for organizations placing on the market or putting into service high-risk AI systems and is not specific to any particular sector.

Artificial Intelligence conformity assessment serves the purpose of providing notice and assurance to stakeholders about conformity against stated requirements. It maps the conformity assessment activities to the different phases of the AI system life cycle. This document provides procedures and processes for conformity assessment activities related to AI systems. The intended audience for this document is primarily conformity assessment scheme developers, owners and operators that evaluate, test, assess and certify AI systems. It is also useful for organizations and people that are not scheme owners or operators, such as AI system stakeholders including AI system developers, providers, customers, partners and regulatory authorities.

This document specifies the evaluation of computer vision systems, in the sense of measuring the quality of a system’s results to assess its functional suitability. It provides a definition of evaluation methods for those systems, together with guidance on how to select, implement and interpret those evaluation methods. This document covers quantitative metrics as well as other evaluation methods. It includes requirements on the implementation of the described metrics, and further requirements on the technical resources involved in the evaluation process.

This document provides guidance and requirements for the creation and management of datasets in the context of AI, including design choices, data collection and preparation. It defines metrics and methodology to assess dataset quality characteristics such as representativeness, relevance, completeness and correctness. This encompasses consideration of any data, including training data, validation data and test data, and to be used in conjunction with any AI technology.

This document addresses organizational and technical solutions aimed at ensuring the cybersecurity of high-risk AI systems over the life cycle, appropriate to the relevant circumstances and the risks. The technical solutions to address AI-specific vulnerabilities include, where appropriate, measures to prevent, detect, respond to, resolve and control for attacks trying to manipulate the training dataset (data poisoning), or pre-trained components used in training (model poisoning), inputs designed to cause the model to make a mistake (adversarial examples or model evasion), confidentiality attacks or model flaws. This document provides objective criteria to enable decisions on whether a given technical or organizational solution adequately achieves a given vulnerability-related goal.

This document provides terminology, concepts, requirements, and guidance for robustness of AI systems. It is primarily intended for organizations placing on the market or putting into service AI systems and is not specific to any particular sector

This document provides terminology, concepts, requirements, and guidance for accuracy of AI systems. It is primarily intended for organizations placing on the market or putting into service AI systems and is not specific to any particular sector

This document provides terminology, concepts, requirements, and guidance for humanoversight of AI systems. It is primarily intended for organizations placing on the market or putting into service AI systems and is not specific to any particular sector;

This document provides terminology, concepts, requirements, and guidance for transparency of AI systems. It is primarily intended for organizations placing on the market or putting into service AI systems and is not specific to any particular sector

This document defines concepts, measures and requirements for assessment and treatment of bias in AI systems. This includes bias unwanted by the AI Provider and AI Deployer according to their specification of the AI system, in the context of the AI Act. This encompasses consideration of data bias including any data used to build or assess the AI system, but also system or model bias that can result from algorithmic factors, such as algorithm design choices.