EU AI Act Art. 53: GPAI provider obligations
GPAI provider obligations, incl. a public summary of training content on an AI Office template
AIGE-OBL-EUAIA-ART53. Drawn from chapter 08.
Text alternative
- Clause: EU AI Act, Art. 53.
- Duty holder: GPAI provider.
- Applies from: 2025-08-02, In force.
- Artefact: Model cards.
- Layer: Layer 02 Inventory & Transparency.
- Evidence record: Dataset card, +2 more.
- Record schemas: Dataset card , Model card , Vendor due-diligence response .
- The same topic in 18 other frameworks; the crosswalk section below links each clause.
- Id
AIGE-OBL-EUAIA-ART53- 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. 53
- Duty holder
- GPAI provider
- Authority
- AI Office
- Applies from
- In force · Obligations from 2025-08-02; enforcement from 2026-08-02
- Later dates
-
- Commission enforcement powers apply
- GPAI models placed on the market before 2025-08-02 comply by this date (Art. 111(3))
- System class
- GPAI model
The artefact that evidences it
Model cards; training-content summary; AIBOM and dataset provenance.
Patterns that build it
- AIBOM (layer 2)
- Vendor / Model Due-Diligence Gate (layer 2 and 5)
- Training-Data Rights Ledger (layer 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)
- 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)
- 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
GPAI and foundation models
- EU AI Act Art. 55 Obligations of providers of general-purpose AI models with systemic risk (core)
- GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (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
IP and copyright
- GPAI Code Copyright 1.1 Draw up, keep up-to-date and implement a copyright policy (core)
- GPAI Code Copyright 1.2 Reproduce and extract only lawfully accessible copyright-protected content (core)
- GPAI Code Copyright 1.3 Identify and comply with rights reservations when crawling the World Wide Web (core)
- GPAI Code Copyright 1.4 Mitigate the risk of copyright-infringing outputs (core)
- NIST AI RMF GOVERN 6.1 GOVERN 6.1: Policies and procedures are in place that address AI risks associated with third-party entities, including risks of infringement of a third-party's intellectual property or other rights (core)
- G7 Code G7 Action 11 Implement data input measures and protect personal data and intellectual property (core)
- China GenAI Measures GenAI Art. 7(2) No infringement of IP rights in training data (core)
- EU AI Act Art. 53(1)(d) Public summary of the content used for training
- GPAI Code Copyright 1.5 Designate a point of contact and enable the lodging of complaints
- NIST AI RMF MAP 4.1 MAP 4.1: Approaches for mapping AI technology and legal risks of its components, including the use of third-party data or software, are in place, followed, and documented, as are risks of infringement of a third party's intellectual property or other rights
- CSA AICM DSP-20 Data Provenance and Transparency
- Singapore GenAI GenAI 2 Data
- China GenAI Measures GenAI Art. 4(3) Respect IP rights and business ethics
Environmental impact
- GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (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
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-ASSURE-012AI Bill of Materials per Build (Assurance and evidence 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-art53.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. 53: GPAI provider obligations (AIGE-OBL-EUAIA-ART53). 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-art53. CC BY 4.0
BibTeX
@misc{aige2026obligation,
author = {Jorge García Aibar},
title = {{EU AI Act Art. 53: GPAI provider obligations (AIGE-OBL-EUAIA-ART53)}},
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-art53},
note = {Version 0.5.0}
}