EU AI Act Art. 50: transparency for certain AI systems

Transparency for certain AI systems: chatbot disclosure; marking and labelling of synthetic content

By Jorge García Aibar Reviewed 2026-09-24

From clause to evidence Build the artefact, then file every output it produces as a record that names AIGE-OBL-EUAIA-ART50. Drawn from chapter 08.

Text alternative

Clause: EU AI Act, Art. 50.
Duty holder: Provider + deployer.
Applies from: 2026-08-02, In force.
Artefact: Content labelling and machine-readable….
Layers: Layer 04, Layer 02.
Evidence record: Deployment decision record, +1 more.
Record schemas: Deployment decision record , Classification decision record .
The same topic in 19 other frameworks; the crosswalk section below links each clause.

Id

AIGE-OBL-EUAIA-ART50

Instrument

EU AI Act (post-Omnibus) law

Compared side by side

ISO 42001 vs EU AI Act · NIST AI RMF vs EU AI Act

Clause

Art. 50

Duty holder

Provider + deployer

Authority

National MSA

Applies from

2026-08-02 In force · marking grace for existing systems to 2026-12-02

Later dates

2026-12-02 Marking grace for existing systems ends

System class

Transparency (Art. 50)

The artefact that evidences it

Content labelling and machine-readable marking (e.g. C2PA-style); chatbot disclosure banner.

Patterns that build it

Downstream Use Register (layer 2 and 1)
Disclosure & Notification Pipeline (layer 5 and 2)

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)
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)
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
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

Content provenance and deepfakes

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 AI Labelling Label Art. 4 Explicit labels for generated content (core)
China AI Labelling Label Art. 5 Implicit (metadata) labels (core)
China Deep Synthesis DeepSyn Art. 17 Conspicuous labels for confusable content (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

Cases that cite this article

Open controls that evidence it

Draft controls in the open control profiles that map to this row: each states a requirement and the evidence it must leave behind.

AIGE-CTL-AGENT-009 Approval log, bound to the call (Agent runtime profile)
AIGE-CTL-AGENT-015 Checkpoints on irreversible actions, failing closed (Agent runtime profile)
AIGE-CTL-DATA-012 Registered Downstream Consumers of Outputs (Data admission and privacy profile)

Source

Chapter 08, section EU AI Act, post-Omnibus, checked against its sources on the review date above.

Machine-readable

This obligation: /api/v1/obligations/aige-obl-euaia-art50.json
The register: /api/v1/obligations.json · CSV
Schema and stability promise: open data and API

Cite this obligation

García Aibar, J. (2026). EU AI Act Art. 50: transparency for certain AI systems (AIGE-OBL-EUAIA-ART50). 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-euaia-art50. CC BY 4.0

BibTeX

@misc{aige2026obligation,
  author       = {Jorge García Aibar},
  title        = {{EU AI Act Art. 50: transparency for certain AI systems (AIGE-OBL-EUAIA-ART50)}},
  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-euaia-art50},
  note         = {Version 0.5.0}
}