M/593 - Artificial Intelligence

This document establishes a systematized framework for the competences of AI ethicists, categorizing them into knowledge, skills and attitudes related to the specific activities and tasks of the role. It specifies requirements and recommendations necessary for individuals to effectively perform as AI ethicists. These competences encompass a strong understanding of European values and fundamental rights, further enhancing the knowledge, skills and attitudes required for this profession.
This document defines the essential concepts and principles inherent to the AI ethicist role. It illustrates a clear, uniform approach to the integral components of this profession.
Moreover, the document outlines how the role of AI ethicists can be seamlessly integrated into a wide variety of organizations. These include, but are not limited to, commercial enterprises, governmental agencies and non-profit organizations.

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.

This document establishes a systematized framework for the competences of AI ethicists, categorizing them into knowledge, skills and attitudes related to the specific activities and tasks of the role. It specifies requirements and recommendations necessary for individuals to effectively perform as AI ethicists. These competences encompass a strong understanding of European values and fundamental rights, further enhancing the knowledge, skills and attitudes required for this profession.
This document defines the essential concepts and principles inherent to the AI ethicist role. It illustrates a clear, uniform approach to the integral components of this profession.
Moreover, the document outlines how the role of AI ethicists can be seamlessly integrated into a wide variety of organizations. These include, but are not limited to, commercial enterprises, governmental agencies and non-profit organizations.

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.

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.

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.

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 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.

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 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.

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 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.

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 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.