M/613 - Artificial Intelligence_Amd 1
Artificial Intelligence_Amd 1 to M/593
Mandate M/613 is an amendment to the original mandate M/593 concerning standardisation in the field of artificial intelligence (AI). Issued by the European Commission, this amendment updates and refines the standardisation requests directed to European Standardisation Organisations (ESOs) such as CEN, CENELEC, and ETSI. The goal is to support the development and harmonisation of AI standards that align with EU policies and regulatory frameworks, ensuring safe, trustworthy, and interoperable AI technologies across the European market. This mandate reflects evolving priorities in AI standardisation to foster innovation while addressing ethical, legal, and technical challenges.
Purpose
This mandate, M/613, serves as an amendment (Amd 1) to the original standardisation mandate M/593 related to Artificial Intelligence (AI). Its purpose is to update or expand the scope of the initial mandate to address evolving needs in the standardisation of AI technologies within the European Union.
Standardisation request
M/613 requests the development, revision, or alignment of European standards concerning Artificial Intelligence, building upon the work initiated under M/593. This may involve specifying requirements, guidelines, or frameworks to ensure interoperability, safety, and trustworthiness of AI systems. The amendment aims to reflect recent advances in AI capabilities and applications, as well as regulatory and policy developments.
Expected deliverables
Deliverables under this mandate amendment likely include updated sets of technical standards, protocols, and normative documents that can be used by industry, regulators, and other stakeholders. These standards should facilitate the harmonisation of AI technologies across the EU, support compliance with legal requirements, and provide a basis for certification or assessment schemes where applicable.
Context
The amendment M/613 is part of the EU's broader strategy to foster trustworthy and human-centric AI by providing a robust standardisation framework. It complements regulatory initiatives, such as the proposed AI Act, aiming to promote innovation while safeguarding fundamental rights and public interests. The continuation and update of the standardisation work through amendments like M/613 reflect the dynamic nature of AI technology and the need for standards to keep pace with technological and societal changes.
The mandate M/613 covers the standardisation work related to artificial intelligence (AI). It includes defining vocabulary, functional safety, robustness, transparency, and ethical aspects of AI systems. The scope addresses AI applications across multiple sectors and products, ensuring the development of harmonised standards to support trustworthy, safe, and reliable AI technologies within the EU market.
General Information
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.
- Standard51 pagesEnglish languagee-Library read for1 day
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.
- Standard51 pagesEnglish languagee-Library read for1 day
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;
- Draft29 pagesEnglish languagee-Library read for1 day
This document describes a taxonomy of the AI tasks related to computer vision. It includes AI tasks pertaining to either the analysis or generation of images and videos.
- Draft30 pagesEnglish languagee-Library read for1 day
This document provides terminology, concepts, requirements, and guidance for logging 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.
- Draft21 pagesEnglish languagee-Library read for1 day
This document specifies requirements and provides guidance for risk management of AI systems. It specifies terminology, principles and a process for risk management.
The process described in this document intends to assist providers of AI systems to identify the hazards associated with the AI systems, to estimate and evaluate the associated risks, to control these risks, and to monitor the effectiveness of the controls. The process described in this document applies to risks to health, safety and fundamental rights associated with an AI system. The process described in this document is applied throughout the life cycle of the AI system.
This document requires providers to establish objective criteria for risk acceptability but does not specify acceptable risk levels.
This document is intended for use by organizations providing AI systems, regardless of their size, nature or location. This document is not intended for managing risk faced by organizations. This document is intended to support the organization in meeting applicable regulatory requirements.
- Draft71 pagesEnglish languagee-Library read for1 day
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.
- Draft56 pagesEnglish languagee-Library read for1 day
This document describes common capabilities, requirements and a supporting information model for logging of events in AI systems.
This document is designed to be used with a risk management system.
- Draft26 pagesEnglish languagee-Library read for1 day
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.
- Draft55 pagesEnglish languagee-Library read for1 day
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;
- Draft29 pagesEnglish languagee-Library read for1 day
This document provides terminology, concepts, requirements, and guidance for logging 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.
- Draft21 pagesEnglish languagee-Library read for1 day
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.
- Draft55 pagesEnglish languagee-Library read for1 day
This document specifies requirements and provides guidance for risk management of AI systems. It specifies terminology, principles and a process for risk management.
The process described in this document intends to assist providers of AI systems to identify the hazards associated with the AI systems, to estimate and evaluate the associated risks, to control these risks, and to monitor the effectiveness of the controls. The process described in this document applies to risks to health, safety and fundamental rights associated with an AI system. The process described in this document is applied throughout the life cycle of the AI system.
This document requires providers to establish objective criteria for risk acceptability but does not specify acceptable risk levels.
This document is intended for use by organizations providing AI systems, regardless of their size, nature or location. This document is not intended for managing risk faced by organizations. This document is intended to support the organization in meeting applicable regulatory requirements.
- Draft71 pagesEnglish languagee-Library read for1 day
This document describes a taxonomy of the AI tasks related to computer vision. It includes AI tasks pertaining to either the analysis or generation of images and videos.
- Draft30 pagesEnglish languagee-Library read for1 day
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.
- Draft56 pagesEnglish languagee-Library read for1 day
This document describes common capabilities, requirements and a supporting information model for logging of events in AI systems.
This document is designed to be used with a risk management system.
- Draft26 pagesEnglish languagee-Library read for1 day
Frequently Asked Questions
A European Standardization Mandate is a formal request from the European Commission to the European Standardization Organizations (CEN, CENELEC, and ETSI) to develop European standards (ENs) in support of EU legislation and policies. Mandates are issued under Regulation (EU) No 1025/2012 and help ensure that products and services meet the essential requirements set out in EU directives and regulations.
M/613 is a European Standardization Mandate titled "Artificial Intelligence_Amd 1 to M/593". Artificial Intelligence_Amd 1 to M/593 There are 16 standards developed under this mandate.
Standards developed in response to a mandate and cited in the Official Journal of the European Union become "harmonized standards". Products manufactured in compliance with harmonized standards benefit from a presumption of conformity with the essential requirements of the corresponding EU directive or regulation, facilitating CE marking and market access across the European Economic Area.