CEN and CENELEC have accepted a standardization request on Artificial Intelligence from the European Commission. In this context, CEN/CLC/JTC 21 is currently developing European standards which, in the future, would be able to provide manufacturers the presumption of conformity with the upcoming Artificial Intelligence Act. In addition to the published documents, CEN/CLC/JTC 21 has also initiated an ambitious work programme with ambitions to publish a number of new standards and technical reports within the next few years.

• prCEN/CLC ISO/IEC/TS 12791 Information technology - Artificial intelligence - Treatment of unwanted bias in classification and regression machine learning tasks: This Technical Specification (TS) is being developed in parallel by ISO/IEC JTC 1/SC 42 and is presently in the process of publication. This document provides mitigation techniques that can be applied throughout the AI system life cycle in order to treat unwanted bias. This document describes how to address unwanted bias in AI systems that use machine learning to conduct classification and regression tasks. This document is applicable to all types and sizes of organization. 
• prCEN/CLC/TR 17894 Artificial Intelligence Conformity Assessment:  This Technical Report (TR) is at an early stage of development and the text is still under drafting. However, the abstract/scope for this document states that is provides 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.
• prCEN/CLC/TR XXX AI Risks - Check List for AI Risks Management: This is still a preliminary work item. However, the abstract/scope this document present that it will provide a check list of risk criteria for assessment guidance as well as risk events and their assessment for any system using AI. It is not intended to 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 will be included in annex. This document is intended to be applicable by all types of organizations including SMEs, large enterprises, public administration etc.
• prCEN/CLC/TR XXX Environmentally sustainable Artificial Intelligence: This document is still under drafting in collaboration with ISO/IEC JTC 1/SC 42. However, the abstract/scope states that the 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.
• prCEN/CLC/TR XXXX Artificial Intelligence - Overview of Al tasks and functionalities related to natural language processing: This document is still under drafting in collaboration with ISO/IEC JTC 1/SC 42.
• prEN ISO/IEC 12792 Information technology - Artificial intelligence - Transparency taxonomy of AI systems: This document is under development in collaboration with ISO/IEC JTC 1/SC 42, and the forecasted date for ballot is 2025-09-30. 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.
• prEN ISO/IEC 23282 Artificial Intelligence - Evaluation methods for accurate natural language processing systems: This document is under development in collaboration with ISO/IEC JTC 1/SC 42, and the forecasted date for ballot for approval as a European standard is 2026-01-02. 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 them. 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.
• prEN ISO/IEC 24029-2 Artificial intelligence (AI) - Assessment of the robustness of neural networks - Part 2: Methodology for the use of formal methods: This document has originally been developed by ISO/IEC JTC 1/SC 42 and published as ISO/IEC 24029-2:2023, however the date for the approval as a European standard is apparently not set yet. This document provides methodology for the use of formal methods to assess robustness properties of neural networks. The document focuses on how to select, apply, and manage formal methods to prove robustness properties.
• prEN ISO/IEC 25059 Software engineering - Systems and software Quality Requirements and Evaluation (SQuaRE) - Quality model for AI systems: This document has originally been developed by ISO/IEC JTC 1/SC 42 and published as ISO/IEC 25059:2023, and the forecasted date for ballot for approval as a European standard is 2025-09-28. This document outlines a quality model for AI systems and is an application-specific extension to the SQuaRE series. 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.
• prEN ISO/IEC 42001 Information technology - Artificial intelligence - Management system: This document has originally been developed by ISO/IEC JTC 1/SC 42 and published as ISO/IEC 42001:2023, however the date for the ballot for approval as a European standard is apparently not set yet. ISO/IEC 42001 is an international standard that specifies requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS) within organizations. It is designed for entities providing or utilizing AI-based products or services, ensuring responsible development and use of AI systems. ISO/IEC 42001 is the world’s first AI management system standard, providing valuable guidance for this rapidly changing field of technology. It addresses the unique challenges AI poses, such as ethical considerations, transparency, and continuous learning. For organizations, it sets out a structured way to manage risks and opportunities associated with AI, balancing innovation with governance.
• prEN ISO/IEC 8183 Information technology - Artificial intelligence - Data life cycle framework: This document has originally been developed by ISO/IEC JTC 1/SC 42 and published as ISO/IEC 8183:2023. However, at CEN/CLC/JTC 21 it is still under enquiry, but the date for ballot for approval as a European standard has been forecasted to be 2025-07-12. This document defines the stages and identifies associated actions for data processing throughout the artificial intelligence (AI) system life cycle, including acquisition, creation, development, deployment, maintenance, and decommissioning. This document does not define specific services, platforms, or tools. This document is applicable to all organizations, regardless of type, size, or nature, that use data in the development and use of AI systems. 
• prEN XXX Competence Requirements for AI ethicists professionals: This is still a preliminary work item. However, the abstract/scope this document present that the purpose of this proposal is to explore the possibility of describing the competence requirements for AI ethicists. The proposal aims at identifying possible requirements and recommendations that are needed for individuals to professionally perform the role of AI ethicist. The proposal wants to contribute to the establishment of a common understanding of the fundamental concepts and principles inherent to the AI ethicist profession. The proposal explores how the role of AI ethicists can be integrated within a different range of organisations, including but not limited to commercial enterprises, government agencies, and not-for-profit organisations.
• prEN XXX AI system logging: This is still a preliminary work item. However, the abstract/scope this document present that the purpose of this document is to provide requirements and technical formats for logging of AI systems in accordance with the record keeping requirements in the AI act. It shall provide a technical format for conformity assessment inputs. Wherever possible, it will use definitions and metrics defined in existing standards. It shall define a syntactical and semantic definition for log formats, including machine-readable schemas. Finally, it will establish recommendations on how data can be transmitted and stored for post-market monitoring purposes. Defining substantial modification is not in scope.
• prEN XXX Data terms measures and bias requirements: This is still a preliminary work item. However, the abstract/scope this document present that the purpose of this proposed European Norm is to define the data terms measurements and bias requirements, addressing shortcomings and monitoring of the data processed by AI systems in the context of avoiding unwanted bias and proxy discrimination.
• prEN XXXXX AI-enhanced nudging: This document is still under drafting. However, the abstract/scope this document present that the purpose of this document is to provide definitions, concepts, and guidelines to address specifically AI-enhanced nudging mechanisms by organisations. It will focus on a standard that aims to support existing legislations and allow industry to deal with AI-enhanced Nudging Mechanisms according to applicable standards, guidelines, and processes. It will be applicable to “AI-enhanced nudging mechanisms” as a sub-category of digital nudges empowered and enhanced by AI systems. AI-enhanced nudging mechanisms can occur at a very fine level of granularity and are difficult to be regulated by hard law or hard ethics. Case studies have shown that although regulations exist at EU level (e.g. GDPR for personal data or UCPD for unfair commercial practices), the subtlety and spread of nudging mechanisms makes it difficult to enforce the law. It also provides use-cases to illustrate the subcategory of digital nudge enhanced by AI systems. It also provides requirements for designing Responsible AI-enhanced nudging mechanisms. Processes and key indicators will accompany requirements, both horizontally (by industry and sectors) and vertically (by applications and technologies), to develop guidance, self-assessment methodologies and methodologies for third-party audits. It is not applicable for nudge mechanisms designed by the architects of the decision-making process and embedded in the interfaces of deterministic systems, where the allocation of moral responsibility is direct (i.e. digital nudging mechanisms not enhanced by AI systems).