GPAI Code Transparency chapter
Up-to-date model documentation for the AI Office and downstream deployers
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 22 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.
Data table: every relation
| Family | Item | Relation |
|---|---|---|
| Same crosswalk topic | AI Verify 1.1.6, 1.7.2, 1.1.1, 9.7.1, 5.3.1, 4.5.2, 9.3.1, 4.1.1, 4.8.1, 11.1.1 | Core clause |
| Same crosswalk topic | CSA AICM MDS-03, MDS-04, STA-10, STA-09, MDS-12 | Core clause |
| Same crosswalk topic | China AI Labelling Label Art. 4, Label Art. 5 | Core clause |
| Same crosswalk topic | China Deep Synthesis DeepSyn Art. 16, DeepSyn Art. 17, DeepSyn Art. 14 | Core clause |
| Same crosswalk topic | China GenAI Measures GenAI Art. 12, GenAI Art. 19, GenAI Art. 7 | Core clause |
| Same crosswalk topic | CoE Convention CoE Art. 14(2), CoE Art. 15(2) | Core clause |
| Same crosswalk topic | EU AI Act Art. 11, Art. 13, Art. 53, Art. 50, Art. 86, Art. 18, Art. 43, Art. 53(1)(d), Art. 50(2), 50(4), Art. 25, Art. 25(4), Art. 26, Art. 22, Art. 23, Art. 24, Art. 54, Art. 55, Art. 51, Art. 56, Annex XI 1(2)(e), Art. 40(2), Art. 95(2)(b) | Core clause |
| Same crosswalk topic | G7 Code G7 Action 3, G7 Action 11, G7 Action 1 | Core clause |
| Same crosswalk topic | GAO AI Accountability 1.9, 3.5, 1.7, 2.6 | Core clause |
| Same crosswalk topic | GDPR Arts. 13–14, Art. 30, Art. 28, Arts. 44–46 | Core clause |
| Same crosswalk topic | GPAI Code Safety C1, Safety 3.2, Safety C7 | Core clause |
| Same crosswalk topic | ISO 42001 7.5, A.6, A.8, A.10 | Core clause |
| Same crosswalk topic | Korea AI Act Art. 31, Art. 34(1)(2), Art. 32 | Core clause |
| Same crosswalk topic | NIST AI RMF MAP 1, MEASURE 2.8, MAP 1.6, MEASURE 2.9, GOVERN 6, MAP 4, MANAGE 3, MANAGE 3.1, GOVERN 6.2, MEASURE 2.12 | Core clause |
| Same crosswalk topic | OECD AI Principles OECD 1.3, OECD 1.1 | Core clause |
| Same crosswalk topic | OWASP Agentic ASI04 | Core clause |
| Same crosswalk topic | OWASP LLM LLM04:2026 | Core clause |
| Same crosswalk topic | Singapore GenAI GenAI 3, GenAI 8, GenAI 9 | Core clause |
| Same crosswalk topic | TC260 Framework 3.0 TC260 App. 2 II.4, TC260 4.4.4 | Core clause |
| Same crosswalk topic | UK ATRS ATRS Tier 1, ATRS 2.2, ATRS 2.1.4 | Core clause |
| Same crosswalk topic | China Algo. Rec. AlgoRec Art. 16 | Related clause |
| Same crosswalk topic | GB/T 45654 GB/T 45654 Content labelling | Related clause |
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)
- AI Verify 1.1.6 Transparency: publicly report accuracy, intended use cases, limitations, risk assessment and mitigations to stakeholders (core)
- AI Verify 1.7.2 Transparency: make model or system cards publicly available where warranted by public interest (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
- AI Verify 1.1.1 Transparency: design an in-house policy on communication to consumers that articulates the principles for transparency
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)
- AI Verify 9.7.1 Accountability: clear responsibilities between parties in the supply chain, set in service level agreements and contracts (core)
- AI Verify 5.3.1 Security: secure the supply chain by assessing and monitoring its security risks across the life cycle (core)
- AI Verify 4.5.2 Safety: continuously monitor third-party systems, with a fallback plan and contracts that assign liability (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
- AI Verify 9.3.1 Accountability: select AI system suppliers aligned with the organisation's approach, with remedial action when they underperform
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
- AI Verify 4.1.1 Safety (technical test): run benchmarks and red-teaming to evaluate a generative model for safety and possible harms
- AI Verify 4.8.1 Safety: identify and prevent content that violates laws or is harmful, including CBRN information and offensive cyber capabilities
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)
- AI Verify 11.1.1 Inclusive growth, societal and environmental well-being: determine that the AI system benefits the environment (e.g. energy and water use) (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}
}