GPAI Code Copyright chapter
A policy to comply with Union copyright law, incl. respecting reservations of rights
AIGE-OBL-GPAICOP-COPYRIGHT. Drawn from chapter 08.
Text alternative
- Clause: GPAI Code, Copyright chapter.
- Duty holder: Not stated.
- Applies from: No date, Voluntary.
- Artefact: Training-data provenance and licence….
- Layers: Layer 01, Layer 02.
- Evidence record: Evidence record v1.
- Record schema: Evidence record.
- The same topic in 14 other frameworks; the crosswalk section below links each clause.
- Id
AIGE-OBL-GPAICOP-COPYRIGHT- Instrument
- GPAI Code of Practice code
- Clause
- Copyright chapter
- Applies from
- No date Voluntary · published 2025-07-10
The artefact that evidences it
Training-data provenance and licence records; policy-as-code for source filtering.
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.
Data governance
- EU AI Act Art. 10 Data and data governance (core)
- ISO 42001 A.7 Data for AI systems (core)
- TC260 Framework 3.0 TC260 2.1.3 Data safety risks (core)
- China GenAI Measures GenAI Art. 7 Training-data lawful sourcing (core)
- China GenAI Measures GenAI Art. 8 Data-annotation standards (core)
- China GenAI Measures GenAI Art. 11 Protection of user input and records (core)
- China Deep Synthesis DeepSyn Art. 14 Training-data management (core)
- GB/T 45654 GB/T 45654 Corpus security Training-corpus (data) security requirements (core) (clause not verified)
- EU AI Act Art. 10(2)(f)–(g) Examination for possible biases; measures to detect, prevent and mitigate them (core)
- GDPR Art. 5(1)(c) Data minimisation (core)
- GDPR Art. 25 Data protection by design and by default (core)
- ISO 42001 A.7.3 Acquisition of data (core) (clause not verified)
- CSA AICM DSP-20 Data Provenance and Transparency (core)
- UK ATRS ATRS 2.4.3 Development data specification (core)
- Singapore GenAI GenAI 2 Data (core)
- GAO AI Accountability 2.1 Sources: document sources and origins of data used to develop the models (core)
- GAO AI Accountability 2.2 Reliability: assess reliability of data used to develop the models (core)
- EU AI Act Art. 4a Special-category data for bias detection
- ISO 42001 A.4 Resources for AI systems
- NIST AI RMF MAP 2 MAP 2: Categorization of the AI system is performed
- NIST AI RMF MEASURE 2.10 MEASURE 2.10: Privacy risk of the AI system is examined and documented
- NIST AI RMF MEASURE 2.11 MEASURE 2.11: Fairness and bias are evaluated and results are documented
- TC260 Framework 3.0 TC260 5.1 Model R&D safety guidelines
- EU AI Act Art. 53 Obligations for providers of general-purpose AI models
- EU AI Act Art. 53(1)(c) Copyright policy, including rights reservations
- EU AI Act Art. 5(1)(e) Prohibited: untargeted scraping of facial images
- GDPR Art. 9 Processing of special categories of personal data
- CSA AICM DSP-21 Data Poisoning Prevention & Detection
- OWASP LLM LLM05:2026 Data and Model Poisoning
- GAO AI Accountability 2.4 Variable selection: assess data variables used in the AI component models
- GAO AI Accountability 2.5 Enhancement: assess the use of synthetic, imputed, and/or augmented data
IP and copyright
- EU AI Act Art. 53(1)(c) Copyright policy, including rights reservations (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
- 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
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-copyright.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 Copyright chapter (AIGE-OBL-GPAICOP-COPYRIGHT). 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-copyright. CC BY 4.0
BibTeX
@misc{aige2026obligation,
author = {Jorge García Aibar},
title = {{GPAI Code Copyright chapter (AIGE-OBL-GPAICOP-COPYRIGHT)}},
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-copyright},
note = {Version 0.5.0}
}