GPAI Code Transparency chapter

Up-to-date model documentation for the AI Office and downstream deployers

By Jorge García Aibar Reviewed 2026-09-19

From clause to evidence Build the artefact, then file every output it produces as a record that names AIGE-OBL-GPAICOP-TRANSPARENCY. Drawn from chapter 08.

Text alternative

Clause: GPAI Code, Transparency chapter.
Duty holder: Not stated.
Applies from: No date, Voluntary.
Artefact: Model cards.
Layer: Layer 02 Inventory & Transparency.
Evidence record: Evidence record v1.
Record schema: Evidence record.
The same topic in 21 other frameworks; the crosswalk section below links each clause.

Id: AIGE-OBL-GPAICOP-TRANSPARENCY
Instrument: GPAI Code of Practice code
Clause: Transparency chapter
Applies from: No date Voluntary · published 2025-07-10

The artefact that evidences it

Model cards; structured model documentation; AIBOM.

L2 Inventory & Transparency

Patterns that build it

No pattern in the catalogue names this clause on its "Maps to" line yet; the artefact above is the engineering answer.

The same topic in other frameworks

From the topic crosswalk: the clauses filed under the same topics as this one. Mappings are illustrative, not a claim of conformity.

Documentation and transparency

EU AI Act Art. 11 Technical documentation (core)
EU AI Act Art. 13 Transparency and provision of information to deployers (core)
EU AI Act Art. 53 Obligations for providers of general-purpose AI models (core)
ISO 42001 7.5 Documented information (core)
ISO 42001 A.6 AI system life cycle (core)
ISO 42001 A.8 Information for interested parties (core)
China AI Labelling Label Art. 4 Explicit labels for generated content (core)
China AI Labelling Label Art. 5 Implicit (metadata) labels (core)
China GenAI Measures GenAI Art. 12 Labelling of generated content (core)
China Deep Synthesis DeepSyn Art. 16 Implicit technical labels (core)
China Deep Synthesis DeepSyn Art. 17 Conspicuous labels for confusable content (core)
GDPR Arts. 13–14 Information to be provided to the data subject (core)
CSA AICM MDS-03 Model Documentation (core)
Korea AI Act Art. 31 Transparency: prior notice, output labelling, realistic synthetic content (core)
UK ATRS ATRS Tier 1 Summary information (core)
Singapore GenAI GenAI 3 Trusted Development and Deployment (core)
CoE Convention CoE Art. 14(2) Documentation sufficient to contest decisions; complaint to authorities (core)
OECD AI Principles OECD 1.3 Transparency and explainability (core)
G7 Code G7 Action 3 Publicly report capabilities, limitations and domains of use (core)
GAO AI Accountability 1.9 Transparency: enable external stakeholders to access information on the design, operation, and limitations of the AI system (core)
GAO AI Accountability 3.5 Documentation: document the methods for assessment, performance metrics, and outcomes of the AI system (core)
EU AI Act Art. 50 Transparency obligations for providers and deployers of certain AI systems
NIST AI RMF MAP 1 MAP 1: Context is established and understood
NIST AI RMF MEASURE 2.8 MEASURE 2.8: Risks associated with transparency and accountability are examined and documented
China GenAI Measures GenAI Art. 19 Disclosure to regulators
China Algo. Rec. AlgoRec Art. 16 Notice that recommendation is used
GB/T 45654 GB/T 45654 Content labelling Generated-content labelling requirements (clause not verified)
EU AI Act Art. 86 Right to explanation of individual decision-making
EU AI Act Art. 18 Documentation keeping
EU AI Act Art. 43 Conformity assessment
EU AI Act Art. 53(1)(d) Public summary of the content used for training
EU AI Act Art. 50(2), 50(4) Machine-readable marking of synthetic content; disclosure of deep fakes
GDPR Art. 30 Records of processing activities
NIST AI RMF MAP 1.6 MAP 1.6: System requirements are elicited from and understood by relevant AI actors. Design decisions take socio-technical implications into account to address AI risks
NIST AI RMF MEASURE 2.9 MEASURE 2.9: The AI model is explained, validated, and documented, and AI system output is interpreted within its context as identified in the MAP function to inform responsible use and governance
CSA AICM MDS-04 Model Documentation Requirements
Korea AI Act Art. 34(1)(2) Explanation plan: result, main criteria, training-data overview
UK ATRS ATRS 2.2 Description and rationale
CoE Convention CoE Art. 15(2) Notification of interaction with an AI system
GAO AI Accountability 1.7 Specifications: establish and document technical specifications

Supply chain and third parties

EU AI Act Art. 25 Responsibilities along the AI value chain (core)
ISO 42001 A.10 Third-party and customer relationships (core)
NIST AI RMF GOVERN 6 GOVERN 6: Policies and procedures are in place to address AI risks and benefits arising from third-party software and data and other supply chain issues (core)
NIST AI RMF MAP 4 MAP 4: Risks and benefits are mapped for all AI system components including third-party software and data (core)
NIST AI RMF MANAGE 3 MANAGE 3: AI risks and benefits from third-party entities are managed (core)
TC260 Framework 3.0 TC260 App. 2 II.4 Supply chain and tool management (core)
EU AI Act Art. 25(4) Written agreement with third-party suppliers (core)
GDPR Art. 28 Processor (core)
NIST AI RMF MANAGE 3.1 MANAGE 3.1: AI risks and benefits from third-party resources are regularly monitored, and risk controls are applied and documented (core)
CSA AICM STA-10 Supply Chain Risk Management (core)
CSA AICM STA-09 Service Bill of Material (BOM) (core)
OWASP LLM LLM04:2026 Supply Chain (core)
OWASP Agentic ASI04 Agentic Supply Chain Vulnerabilities (core)
EU AI Act Art. 26 Obligations of deployers of high-risk AI systems
TC260 Framework 3.0 TC260 4.4.4 Open-source ecosystem
China GenAI Measures GenAI Art. 7 Lawful data and model sources
China Deep Synthesis DeepSyn Art. 14 Providers and technical supporters
EU AI Act Art. 22 Authorised representatives of providers of high-risk AI systems
EU AI Act Art. 23 Obligations of importers
EU AI Act Art. 24 Obligations of distributors
EU AI Act Art. 54 Authorised representatives of providers of general-purpose AI models
GDPR Arts. 44–46 Transfers to third countries
NIST AI RMF GOVERN 6.2 GOVERN 6.2: Contingency processes are in place to handle failures or incidents in third-party data or AI systems deemed to be high-risk
UK ATRS ATRS 2.1.4 Third party involvement
G7 Code G7 Action 11 Implement data input measures and protect personal data and intellectual property
GAO AI Accountability 2.6 Dependency: assess interconnectivities and dependencies of data streams that operationalize the AI system

GPAI and foundation models

EU AI Act Art. 53 Obligations for providers of general-purpose AI models (core)
EU AI Act Art. 55 Obligations of providers of general-purpose AI models with systemic risk (core)
GPAI Code Safety C1 Commitment 1: Safety and Security Framework (core)
Korea AI Act Art. 32 Safety duties for AI above the compute threshold (core)
EU AI Act Art. 51 Classification of general-purpose AI models as general-purpose AI models with systemic risk
EU AI Act Art. 56 Codes of practice
GPAI Code Safety 3.2 Measure 3.2: Model evaluations
GPAI Code Safety C7 Commitment 7: Safety and Security Model Reports
CSA AICM MDS-12 Open Model Risk Assessment
CSA AICM MDS-03 Model Documentation
Singapore GenAI GenAI 8 Safety and Alignment R&D
G7 Code G7 Action 1 Identify, evaluate and mitigate risks across the lifecycle, including testing
China GenAI Measures GenAI Art. 7 Lawful data and foundation-model sources

Environmental impact

EU AI Act Annex XI 1(2)(e) Known or estimated energy consumption of the GPAI model (core)
NIST AI RMF MEASURE 2.12 MEASURE 2.12: Environmental impact and sustainability of AI model training and management activities as identified in the MAP function are assessed and documented (core)
OECD AI Principles OECD 1.1 Inclusive growth, sustainable development and well-being (core)
EU AI Act Art. 40(2) Standardisation deliverables on energy and resource performance
EU AI Act Art. 95(2)(b) Codes of conduct: environmental sustainability
Singapore GenAI GenAI 9 AI for Public Good

Source

Chapter 08, section GPAI Code of Practice, checked against its sources on the review date above.

Machine-readable

This obligation: /api/v1/obligations/aige-obl-gpaicop-transparency.json
The register: /api/v1/obligations.json · CSV
Schema and stability promise: open data and API

Cite this obligation

García Aibar, J. (2026). GPAI Code Transparency chapter (AIGE-OBL-GPAICOP-TRANSPARENCY). In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0). https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/obligations/aige-obl-gpaicop-transparency. CC BY 4.0

BibTeX

@misc{aige2026obligation,
  author       = {Jorge García Aibar},
  title        = {{GPAI Code Transparency chapter (AIGE-OBL-GPAICOP-TRANSPARENCY)}},
  howpublished = {In AI Governance Engineering: The Thesis \& Body of Knowledge},
  year         = {2026},
  version      = {0.5.0},
  doi          = {10.5281/zenodo.22956197},
  url          = {https://aigovernanceengineer.com/obligations/aige-obl-gpaicop-transparency},
  note         = {Version 0.5.0}
}