ETSI TR 104 119 V1.1.1 (2025-09)

Methods for Testing & Specification (MTS); AI Testing; Guidelines for Documentation of AI-enabled Systems

DTR/MTS-104119

General Information

• Status: Not Published
• Technical Committee: MTS - Methods for Testing & Specification
• Current Stage: 12 - Citation in the OJ (auto-insert)
• Due Date: 30-Sep-2025
• Completion Date: 18-Sep-2025

Standards Content (Sample)

TECHNICAL REPORT
Methods for Testing & Specification (MTS);
AI Testing;
Guidelines for Documentation of
AI-enabled Systems
2 ETSI TR 104 119 V1.1.1 (2025-09)

Reference
DTR/MTS-104119
Keywords
AI, testing
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ETSI
3 ETSI TR 104 119 V1.1.1 (2025-09)

Contents
Intellectual Property Rights . 6
Foreword . 6
Modal verbs terminology . 6
1 Scope . 7
2 References . 7
2.1 Normative references . 7
2.2 Informative references . 7
3 Definition of terms, symbols and abbreviations . 12
3.1 Terms . 12
3.2 Symbols . 14
3.3 Abbreviations . 14
4 Purpose of Documentation . 14
5 Motivation for a Harmonized Documentation Scheme . 16
5.1 Current AI Documentation Schemes . 16
5.2 Gaps to EU AI Act Requirements . 16
5.3 Summary . 17
6 Approach for Documenting AI-enabled Systems . 18
6.1 Overview . 18
6.2 Documentation items (what to document) . 18
6.3 Documentation stakeholders (roles) . 19
6.3.1 General . 19
6.3.2 Audience analysis . 20
6.3.3 Stakeholder categories and documentation requirements . 21
6.3.3.1 AI Provider . 21
6.3.3.2 AI Producer . 21
6.3.3.3 AI Customer . 22
6.3.3.4 AI Partner . 22
6.3.3.5 AI Subject . 22
6.3.3.6 Relevant Authorities. 22
6.4 AI-system life cycle. 23
6.5 Documentation Techniques for Effective Information Management . 25
6.5.1 General . 25
6.5.2 (Motivation and) Overview. 26
6.5.3 Questionnaires . 26
6.5.4 Information Sheets . 26
6.5.5 Checklists . 27
6.5.5 Templates . 27
6.5.6 White Papers . 27
6.5.7 Knowledge Graphs . 28
6.5.8 Visual Techniques (Diagrams, Flowcharts, Infographics) . 28
6.5.9 Interactive Techniques . 28
6.5.10 Domain-Specific Language (DSL) . 28
6.5.11 Summary . 29
6.6 Quality aspects of documentation. 31
6.6.1 General . 31
6.6.2 Correctness . 31
6.6.3 Consistency . 31
6.6.4 Comprehensibility . 31
6.6.5 Conciseness. 31
6.6.6 Minimalism . 31
6.6.7 Accessibility . 32
6.6.8 Systematic understanding . 32
6.7 Documentation Approach . 32
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4 ETSI TR 104 119 V1.1.1 (2025-09)

7 Guidance for EU AI Act Compliant Documentation . 33
7.1 Introduction to the EU AI Act . 33
7.2 Mapping of EU AI Act Stakeholder . 35
7.3 Documentation Guidance for High-Risk AI Systems . 36
7.3.1 General . 36
7.3.2 Risk Management System (Art. 9) . 37
7.3.3 Data and Data Governance (Art. 10) . 38
7.3.4 Record-Keeping (Art. 12) . 39
7.3.5 Transparency and Information to Deployers (Art. 13) . 40
7.3.6 Human Oversight (Art. 14) . 40
7.3.7 Accuracy, Robustness, and Cybersecurity (Art. 15) . 41
7.3.8 Technical Documentation (Art. 11) . 42
7.4 Documentation Requirements for GPAI models . 45
7.4.1 General . 45
7.4.2 GPAI Models without Systemic Risk . 45
7.4.3 GPAI Models with Systemic Risk . 45
7.4.4 Documentation . 46
Annex A: Sample Documentation Scenarios . 47
A.1 Healthcare Use Case . 47
A.1.1 Use Case Description . 47
A.1.2 Documentation Approach . 47
A.1.2.1 Key Documentation Requirements . 47
A.1.2.2 Example 1: General AI System Description . 47
A.1.2.3 Example 2: Design and Development Documentation . 49
A.2 AI-Based Person Detection for Construction Machinery . 52
A.2.1 Use Case Description . 52
A.2.2 Documentation Approach . 52
A.2.2.1 Key Documentation Requirements . 52
A.2.2.2 Example 1: General AI System Description . 52
A.2.2.3 Example 2: Data Documentation . 53
Annex B: Trustworthy AI: Definition and core characteristics . 55
B.1 Definition of Trustworthy AI . 55
B.2 Relevant frameworks and guidelines . 56
B.3 Operationalization of Trustworthiness in the EU AI Act . 57
Annex C: Risk Mitigation by Documentation . 58
Annex D: Documentation Schemes and Gap Analysis to the EU AI Act . 62
D.1 Data-Focused Documentation Approaches . 62
D.1.1 Datasheet for Datasets . 62
D.1.2 DescribeML . 62
D.1.3 Dataset Nutrition Label . 63
D.1.4 Data Cards . 63
D.1.5 Dataset Development Life Cycle Documentation Framework . 63
D.2 Model-And-Method-Focused Documentation Approaches . 64
D.2.1 Model Cards . 64
D.2.2 Method Card . 65
D.3 System-Focused Documentation Approaches . 65
D.3.1 FactSheets. 65
D.3.2 System Cards . 66
D.4 Domain Specific Documentation Approaches . 66
D.4.1 Model Facts Label . 66
D.4.2 Risk Cards . 66
D.4.3 Datasheet for Subjective and Objective Quality Assessment Datasets. 67
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5 ETSI TR 104 119 V1.1.1 (2025-09)

D.4.4 Assurance Cases to document the reasoning behind other documented artifacts . 67
D.5 Gap Analysis to EU AI Act . 68
History . 71

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6 ETSI TR 104 119 V1.1.1 (2025-09)

Intellectual Property Rights
Essential patents
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pertaining to these essential IPRs, if any, are publicly available for ETSI members and non-members, and can be
found in ETSI SR 000 314: "Intellectual Property Rights (IPRs); Essential, or potentially Essential, IPRs notified to
ETSI in respect of ETSI standards", which is available from the ETSI Secretariat. Latest updates are available on the
ETSI IPR online database.
Pursuant to the ETSI Directives including the ETSI IPR Policy, no investigation regarding the essentiality of IPRs,
including IPR searches, has been carried out by ETSI. No guarantee can be given as to the existence of other IPRs not
referenced in ETSI SR 000 314 (or the updates on the ETSI Web server) which are, or may be, or may become,
essential to the present document.
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Foreword
This Technical Report (TR) has been produced by ETSI Technical Committee Methods for Testing and Specification
(MTS).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.

ETSI
7 ETSI TR 104 119 V1.1.1 (2025-09)

1 Scope
The present document provides guidelines and recommendations for documentation schemes that support the
continuous and consistent documentation of quality and quality related attributes for AI-enabled systems.
This includes an analysis of current documentation schemes and Use case examples. It also defines a process how to
document AI-enabled systems.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long-term validity.
The following referenced documents may be useful in implementing an ETSI deliverable or add to the reader's
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machinery and repealing Directive 2006/42/EC of the European Parliament and of the Council and
Council Directive 73/361/EEC.
[i.89] ISO/IEC AWI 42102: "Information technology — Artificial intelligence — Taxonomy of AI
system methods and capabilities" [Draft]. Retrieved June 20, 2025.
[i.90] Schmid, T. (2023): "A Systematic and Efficient Approach to the Design of Modular Hybrid AI
Systems". In AAAI Spring Symposium: MAKE.
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[i.91] Holoyad, T., Schmid, T., & Hildesheim, W.: "Managing and understanding artificial intelligence:
From classical to generative AI - A practice guide for decision‑makers, developers and regulators
in the age of the EU AI Act". Springer.
3 Definition of terms, symbols and abbreviations
3.1 Terms
For the purposes of the present document, the following terms apply:
affected person: individuals with limited technical knowledge who may be impacted by AI systems
NOTE: They require protection from arbitrary decisions and risks, and may need to take legal measures if they
feel unfairly treated. They can be represented by NGOs who possess greater capacities, knowledge, and
power. See also: AI subject.
AI customer: organization or entity that uses an AI product or service either directly or by its provision to AI users
NOTE: Aligned with ISO/IEC 22989 [i.4]: AI customer.
AI partner: organization or entity that provides services in the context of AI
NOTE 1: AI partners can perform technical development of AI products or services, conduct testing and validation
of AI products and services, audit AI usage, evaluate AI products or services and perform other tasks.
NOTE 2: This includes roles like AI system integrators, who incorporate AI components into broader systems, data
providers, AI evaluators, and AI auditors.
NOTE 3: Aligned with ISO/IEC 22989 [i.4]: AI partner.
AI producer: organization or entity that designs, develops, tests and deploys products or services that use one or more
AI system
NOTE 1: This includes AI developers, who focus on creating AI models, implementing computational processes,
and verifying both the computation and model performance.
NOTE 2: According to the EU AI Act [i.2] an AI producer is also included in the term Deployer, as "a natural or
legal person, public authority, agency or other body using an AI system under its authority except where
the AI system is used in the course of a personal non-professional activity".
NOTE 3: Aligned with ISO/IEC 22989 [i.4]: AI producer.
AI provider: organization or entity that offers products or services utilizing one or more AI systems
NOTE 1: AI providers include AI platform providers, who enable other stakeholders to produce AI services or
products, and AI service or product providers, who deliver AI solutions directly to customers.
NOTE 2: The EU AI Act [i.2] defines Provider as "a natural or legal person, public authority, agency or other body
that develops an AI system or a general-purpose AI model or that has an AI system or a general-purpose
AI model developed and places it on the market or puts the AI system into service under its own name or
trademark, whether for payment or free of charge".
NOTE 3: Aligned with ISO/IEC 22989 [i.4]: AI provider.
AI subject: organization or entity that is impacted by an AI system, service or product
NOTE 1: This can also include individuals or communities affected by AI applications, such as users of social
networks or drivers of AI-automated vehicles.
NOTE 2: The EU AI Act [i.2] uses the term affected person equivalently.
NOTE 3: Aligned with ISO/IEC 22989 [i.4]: AI subject.
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13 ETSI TR 104 119 V1.1.1 (2025-09)

AI user: organization or entity that uses AI products or services
NOTE 1: AI user is a sub-role of AI customer.
NOTE 2: Aligned with ISO/IEC 22989 [i.4]: AI users.
auditor: professionals with deep technical knowledge and understanding of standards and regulations
NOTE 1: They identify compliance issues and ensure AI systems meet ethical and operational benchmarks,
facilitating successful certification and deployment.
NOTE 2: Aligned with ISO/IEC 22989 [i.4]: AI auditor.
bias: systematic difference in treatment of certain objects, people, or groups in comparison to others
NOTE: Aligned with ISO/IEC 22989 [i.4]: bias.
deployer: entities utilizing AI-based products provided by others, requiring sufficient information to effectively
incorporate these systems into their own products or services
NOTE: The EU AI Act [i.2] uses the term 'deployer' as "a natural or legal person, public authority, agency or
other body using an AI system under its authority except where the AI system is used in the course of a
personal non-professional activity". Se
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