China Measures for Labelling AI-Generated Synthetic Content with GB 45438-2025 (in force 2025-09-01)
Explicit labels (text, audio or graphic) and implicit metadata labels carrying the provider's name or code and a content number; distribution platforms verify metadata and flag suspected AI content
AIGE-OBL-CN-LABEL. Drawn from chapter 08.
Text alternative
- Clause: China AI Labelling, Labelling Measures + GB….
- Duty holder: Not stated.
- Applies from: 2025-09-01, In force.
- Artefact: Provenance and watermarking pipeline….
- Layers: Layer 03, Layer 04.
- Evidence record: Evidence record v1.
- Record schema: Evidence record.
- The same topic in 22 other frameworks; the crosswalk section below links each clause.
Data table: the dates
| Date | Step | Detail |
|---|---|---|
| 2025-09-01 | In force | Binding; in force 2025-09-01, the standard implemented the same day |
- Id
AIGE-OBL-CN-LABEL- Instrument
- China Measures for Labelling AI-Generated Synthetic Content (2025) law
- Clause
- Labelling Measures + GB 45438-2025
- Applies from
- In force · Binding; in force 2025-09-01, the standard implemented the same day
The artefact that evidences it
Provenance and watermarking pipeline emitting the GB 45438 metadata fields; platform-side detection and flagging.
Data table: every relation
| Family | Item | Relation |
|---|---|---|
| Same crosswalk topic | AI Verify 1.1.6, 1.7.2, 1.1.1, 3.4.1, 9.8.1, 5.10.1, 4.9.1, 4.9.2, 1.1.3 | Core clause |
| Same crosswalk topic | CSA AICM MDS-03, MDS-04, LOG-09, LOG-12, MDS-09 | Core clause |
| Same crosswalk topic | China Deep Synthesis DeepSyn Art. 16, DeepSyn Art. 17 | Core clause |
| Same crosswalk topic | China GenAI Measures GenAI Art. 12, GenAI Art. 19 | 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. 12, Art. 26(6), Art. 26, Art. 19, Art. 50(2), Art. 50(4), Art. 3(60) | Core clause |
| Same crosswalk topic | G7 Code G7 Action 3, G7 Action 7 | Core clause |
| Same crosswalk topic | GAO AI Accountability 1.9, 3.5, 1.7, 4.3 | Core clause |
| Same crosswalk topic | GDPR Arts. 13–14, Art. 30 | Core clause |
| Same crosswalk topic | GPAI Code Transparency 1.1, Transparency 1.2 | Core clause |
| Same crosswalk topic | ISO 42001 7.5, A.6, A.8, A.6.2.8 | Core clause |
| Same crosswalk topic | Korea AI Act Art. 31, Art. 34(1)(2), Art. 34(1)(5) | Core clause |
| Same crosswalk topic | OECD AI Principles OECD 1.3, OECD 1.5(b) | Core clause |
| Same crosswalk topic | Singapore GenAI GenAI 3, GenAI 7 | Core clause |
| Same crosswalk topic | TC260 Framework 3.0 TC260 App. 2 II.6, TC260 5.3.6 | Core clause |
| Same crosswalk topic | UK ATRS ATRS Tier 1, ATRS 2.2 | Core clause |
| Same crosswalk topic | prEN 18229-1 prEN 18229-1 | 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 |
| Same crosswalk topic | NIST AI RMF MAP 1, MEASURE 2.8, MAP 1.6, MEASURE 2.9, MANAGE 4, MEASURE 3 | Related clause |
| Same crosswalk topic | OWASP LLM LLM07:2026 | Related clause |
| Same crosswalk topic | Singapore Agentic Agentic 2.3.3 | 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 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)
- GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (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
- GPAI Code Transparency 1.2 Providing relevant information
- 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
Logging and traceability
- EU AI Act Art. 12 Record-keeping (core)
- ISO 42001 A.6 AI system life cycle (core)
- TC260 Framework 3.0 TC260 App. 2 II.6 Continuous monitoring and auditing (core)
- TC260 Framework 3.0 TC260 5.3.6 Logs kept and audited (core)
- EU AI Act Art. 26(6) Deployers keep the automatically generated logs (core)
- ISO 42001 A.6.2.8 AI system recording of event logs (core) (clause not verified)
- CSA AICM LOG-09 Log Records (core)
- Korea AI Act Art. 34(1)(5) Documents showing the measures taken (core)
- OECD AI Principles OECD 1.5(b) Traceability of datasets, processes and decisions (core)
- prEN 18229-1 prEN 18229-1 AI trustworthiness framework, Part 1: logging (draft; supports Art. 12) (core) (clause not verified)
- GAO AI Accountability 4.3 Traceability: document results of monitoring activities and any corrective actions taken (core)
- AI Verify 3.4.1 Reproducibility: log all inputs and inference outputs of the model and keep them for a reasonable time for review (core)
- AI Verify 9.8.1 Accountability: process or governance-by-design technology for auditability: versioned code, models and data, and an audit trail of deployments and inputs/outputs (core)
- EU AI Act Art. 26 Obligations of deployers of high-risk AI systems
- NIST AI RMF MANAGE 4 MANAGE 4: Risk treatments, including response and recovery, and communication plans for the identified and measured AI risks are documented and monitored
- NIST AI RMF MEASURE 3 MEASURE 3: Mechanisms for tracking identified AI risks over time are in place
- EU AI Act Art. 19 Automatically generated logs
- CSA AICM LOG-12 Transaction/Activity Logging
- Singapore Agentic Agentic 2.3.3 When deploying, continuously monitor and test
- AI Verify 5.10.1 Security: monitor and log inputs to the AI system (queries, prompts, requests)
Content provenance and deepfakes
- EU AI Act Art. 50(2) Machine-readable marking of synthetic content (core)
- EU AI Act Art. 50(4) Disclosure of deep fakes (core)
- Korea AI Act Art. 31 Transparency: prior notice, output labelling, realistic synthetic content (core)
- Singapore GenAI GenAI 7 Content Provenance (core)
- G7 Code G7 Action 7 Deploy content authentication and provenance mechanisms where feasible (core)
- China Deep Synthesis DeepSyn Art. 17 Conspicuous labels for confusable content (core)
- AI Verify 4.9.1 Safety: establish policies for content provenance: identify provenance harms such as deepfakes and trace content origin (core)
- AI Verify 4.9.2 Safety: evaluate content provenance mechanisms, including the effectiveness and reliability of watermarks (core)
- AI Verify 1.1.3 Transparency: labelling or disclaimers so users know when they interact with an AI system and understand content lineage (core)
- EU AI Act Art. 3(60) Definition of deep fake
- CSA AICM MDS-09 Model Signing/Ownership Verification
- OWASP LLM LLM07:2026 Misinformation
- China GenAI Measures GenAI Art. 12 Labelling of generated content
Source
Chapter 08, section China, checked against its sources on the review date above.
Machine-readable
- This obligation:
/api/v1/obligations/aige-obl-cn-label.json - The register:
/api/v1/obligations.json· CSV - Schema and stability promise: open data and API
Cite this obligation
García Aibar, J. (2026). China Measures for Labelling AI-Generated Synthetic Content with GB 45438-2025 (in force 2025-09-01) (AIGE-OBL-CN-LABEL). 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-cn-label. CC BY 4.0
BibTeX
@misc{aige2026obligation,
author = {Jorge García Aibar},
title = {{China Measures for Labelling AI-Generated Synthetic Content with GB 45438-2025 (in force 2025-09-01) (AIGE-OBL-CN-LABEL)}},
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-cn-label},
note = {Version 0.5.0}
}