Work Programme - AI Standards in Manufacturing
European Standardisation body CEN/CLC/JTC 21
The European Standardization organizations CEN and CENELEC are tasked with developing a broad range of standards commissioned by the EU. Below is an overview of their work program for the upcoming two years.
Although these standards are still under development, they are expected to play a key role in ensuring presumed conformity with the forthcoming AI Act.
This information was last updated in November 2024. For more detailed information on their Artificial Intelligence work program, check out this AIRISE page, or their official webpage.
| Project | Title | Description | Status | |
|---|---|---|---|---|
| EN ISO/IEC 22989:2023/prA1 (WI=JT021031) | Information technology — Artificial intelligence — Artificial intelligence concepts and terminology — Amendment 1 | No information yet | Under Approval | |
| EN ISO/IEC 23053:2023/prA1 (WI=JT021032) | Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML) — Amendment 1 | No information yet | Under Approval | |
| FprEN ISO/IEC 12792 (WI=JT021022) | Information technology - Artificial intelligence - Transparency taxonomy of AI systems (ISO/IEC FDIS 12792:2025) | 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. V02/ | Under Approval | |
| prCEN/CLC/TR XXX (WI=JT021009) | AI Risks - Check List for AI Risks Management | This document provides a check list of risk criteria for assessment guidance as well as risk events and their assessment for any system using AI. It does not offer an explicit method or solution, but rather a set of criteria and possibly measures and contingency plan structure. Detailed examples of risks, harms and possible countermeasures are included in annex. This document is applicable by all types of organizations including SMEs, large enterprises, public administration etc. | Preliminary | |
| prCEN/CLC/TR XXX (WI=JT021026) | Impact assessment in the context of the EU Fundamental Rights | This work item will identify any necessary content to help ensure proper treatment of fundamental rights in the context of the EU AI Act work programme. In order to avoid unnecessary increase in the workload, this item will investigate whether such content can be proposed to augment existing projects (e.g. ISO/IEC 42005, Trustworthiness Framework EN and the Risk Management EN) wherever appropriate. | Preliminary | |
| prCEN/TS (WI=JT021034) | Guidelines on tools for handling ethical issues in AI system life cycle | This document provides specification and guidelines for a set of tools (instruments, activities, procedures) for the practical handling of social and ethical concerns throughout AI systems life cycle: from the emergence of a new issue to the corresponding decision-making. This document offers a list of tools along a minimum set of requirements and guidance that enable organisations to put them into practice. As a general principle, tools provided are independent from any concrete ethical foundation. This document is applicable to all organisations, with any AI stakeholder role, including customers and users. However, it is primarily intended to assist AI systems producers and providers in practical handling of societal concerns and ethical considerations issues during the AI systems life cycle. The document expands on existing standards about: guidance on addressing societal concerns and ethical considerations, AI system life cycle processes, AI system governance, management system, and impact assessment. | Preliminary | |
| prCEN/TS (WI=JT021035) | Sustainable Artificial Intelligence – Guidelines and metrics for the environmental impact of artificial intelligence systems and services | This document describes the principles and framework for environmental impact measurement of artificial intelligence systems and services and provides guidelines for impact reduction throughout the lifecycle. It includes a framework for defining the environmental impact of artificial intelligence, aharmonized calculation method for assessing the environmental impact of artificial intelligence systems and services, reporting guidelines as well as best practices for reducing the environmental impact of AI systems and services throughout their lifecycle. This document is aimed at organizations developing AI systems and services and organizations using AI systems and services, but also at all actors in the value chain who use AI systems and services. | Preliminary | |
| prCEN/TS (WI=JT021033) | Guidance for upskilling organisations on AI ethics and social concerns | The document will provide guidance for upskilling on AI ethics and social concerns in organisations. The document will complement existing standards related to AI ethics and social concerns. Its ultimate objective is to enable organisations to enhance their basic capabilities and culture regarding AI ethics and social concerns, both at the individual level (for employees in any position, excepted AI ethics professionals) and at the organisational level (including AI development teams, departments, board, managers, etc.) In particular, but not limited, to develop continuous learning programs that could include basic capabilities on AI ethics and social concerns This document is applicable to all organisations regardless of AI stakeholder role. | Preliminary | |
| prEN 18228 (WI=JT021024) | AI Risk Management | This document specifies requirements on risk management for AI systems. This document also provides clear and actionable guidance on how risk can be addressed and mitigated throughout the entire lifecycle of the AI system. It applies to risk management for a broad range of products and services which use AI technology, including explicit considerations for vulnerable people. Risks covered include both risks to health and safety and risks to fundamental rights which can arise from AI systems, with impact for individuals, organisations, market and society. This document also defines methods that can be used to determine if a package of risk management measures associated with an AI system will be able to ensure that certain risks arising from that product or system are identified, monitored, and managed, leading to an acceptable level of risk. This document is intended for use by organizations and individuals providing, using, or being affected by products or services that use AI technology, no matter what their size, nature, or location is. The included requirements and guidance have however been specifically tailored to support organisations and individuals who operate inside of the European Union, as well as organisations and individuals outside of the Union who are active in the European Union market or who intend to enter that market. They have been tailored to support these organisations and individuals in meeting applicable regulatory requirements, with the flexibility to accommodate additional expectations from parties they may interact with | Under Drafting | |
| prEN 18229 (WI=JT021008) | AI trustworthiness framework | This document provides a framework for AI systems trustworthiness which contains terminology, concepts, high-level horizontal requirements, guidance and a method to contextualize those to specific stakeholders, domains or applications. The high-level horizontal requirements address foundational aspects and characteristics of trustworthiness of AI systems. This document is primarily intended for organization placing on the market or putting into service AI systems. | Under Drafting | |
| prEN ISO/IEC 23282 (WI=JT021012) | Artificial Intelligence - Evaluation methods for accurate natural language processing systems | This document specifies the evaluation of natural language processing 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. | Under Drafting | |
| prEN ISO/IEC 24970 (WI=JT021021) | Artificial intelligence — AI system logging | 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. | Under Drafting | |
| prEN ISO/IEC 25029 (WI=JT021046) | Artificial intelligence - AI-enhanced nudging | Under Drafting | ||
| prEN ISO/IEC 25059 rev (WI=JT021027) | Software engineering - Systems and software Quality Requirements and Evaluation (SQuaRE) - Quality model for AI systems (ISO/IEC 25059:2023) | This document outlines quality models for AI systems and services and is an applicationspecific extension to the standards on SQuaRE. The characteristics and sub-characteristics detailed in the models provide consistent terminology for specifying, measuring and evaluating AI system and service quality. The characteristics and sub-characteristics detailed in the models also provide a set of quality characteristics against which stated quality requirements can be compared for completeness. | Under Drafting | |
| prEN ISO/IEC 42102 (WI=JT021045) | Information technology - Artificial intelligence – Taxonomy of AI system methods and capabilities | This document provides guidance on the classification of AI system by describing a taxonomy of methods and capabilities. The taxonomy enables AI stakeholders to describe and have a common understanding of an AI system. This document applies to all types of organizations involved in any of the lifecycle stages of AI systems as well as to any AI stakeholder roles | Under Drafting | |
| prEN ISO/IEC TR 23281 (WI=JT021002) | Artificial Intelligence - Overview of Al tasks and functionalities related to natural language processing | This document describes the concept of AI task as applied to natural language. It proposes a landscaping of the AI tasks related to the analysis or generation of natural language, as well as other languagerelated functionalities that are associated to those AI systems. It identifies existing and competing terminologies, co-existing variants of the same tasks and functionalities, and how specific tasks can be affected by language diversity in terms of their role or their challenges. This includes all languages, dialects and variants, whether official or not. The relations among tasks or functionalities, and their interactions within pipelines, are discussed and illustrated. In addition, the document provides references to existing standards and published guidelines associated to those tasks and functionalities, highlighting their differences in case of competing standards. | Under Drafting | |
| prEN XXX (WI=JT021025) | Artificial Intelligence – Evaluation methods for accurate computer vision systems | 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. | Under Drafting | |
| prEN XXX (WI=JT021038) | AI Conformity assessment framework | 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. | Under Drafting | |
| prEN XXX (WI=JT021019) | Competence requirements for professional AI ethicists | This document provides a systematized framework for the competencies of AI ethicists, categorizing them into knowledge, skills, and attitudes related to the specific activities and tasks of the role. It identifies requirements and recommendations necessary for individuals to effectively perform as AI ethicists. These competencies encompass a strong understanding of European values and fundamental rights, further enhancing the knowledge, skills, and attitudes required for this profession. The document aims to foster a shared understanding of 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, government agencies, and non-profit organizations. | Under Drafting | |
| prEN XXX (WI=JT021039) | Artificial intelligence - Quality management system for EU AI Act regulatory purposes | This document specifies the requirements and provides guidance for a quality management system for organizations that provide AI systems. This document is intended to support the organization in meeting applicable regulatory requirements. | Under Drafting | |
| prEN XXX (WI=JT021036) | Artificial Intelligence - Concepts, measures and requirements for managing bias in AI systems | 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. | Under Drafting | |
| prEN XXX (WI=JT021044) | Artificial Intelligence - Taxonomy of AI tasks in computer vision | 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. | Under Drafting | |
| prEN XXX (WI=JT021029) | Artificial intelligence - Cybersecurity specifications for AI Systems | This document addresses organizational and technical solutions aimed at ensuring the cybersecurity of high-risk AI systems over the lifecycle, 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. | Under Drafting | |
| prEN XXX (WI=JT021037) | Artificial Intelligence -- Quality and governance of datasets in AI | 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. | Under Drafting | |
| (WI=JT021030) | Contributions towards ISO/IEC 27090 | Preliminary |