Copyright Policy: [General-purpose AI model provider]
Copyright policy for a provider of a general-purpose AI (GPAI) model,
organised on Article 53(1)(c) of the EU Artificial Intelligence Act
(Regulation (EU) 2024/1689 as amended by Regulation (EU) 2026/1744)
and the five measures of the Copyright chapter of the General-Purpose
AI Code of Practice (published 10 July 2025). It covers lawful access
when crawling, identifying and honouring text and data mining (TDM)
rights reservations under Article 4(3) of Directive (EU) 2019/790,
crawler transparency, safeguards against infringing outputs, and a
point of contact and complaints process for rightsholders. Replace
every [bracketed] value and every row marked Example. GPAI
obligations have applied since 2 August 2025 (2 August 2027 for models
placed on the market before 2 August 2025); check the consolidated
text. This is a documentation format, not legal advice: check the
current official text and a qualified professional.
Document control
| Field | Value |
|---|
| Document ID | [CPY-001] |
| Provider | [Legal name, address] |
| Models covered | [Model names and versions] |
| Code of Practice signatory | [Yes, Copyright chapter / No, alternative means] |
| Version | [1.0] |
| Status | [Draft / Approved / Superseded] |
| Policy owner | [@copyright-policy-owner] |
| Oversight | [@general-counsel], [@data-governance-lead], [@head-of-research] |
| Effective date | YYYY-MM-DD |
| Next review | YYYY-MM-DD |
| Public summary URL | [URL] |
| Classification | [Internal, summary public] |
Approvals
| Role | Name | Decision | Date |
|---|
| General counsel | [@name] | [Approve] | YYYY-MM-DD |
| Chief technology officer | [@name] | [Approve] | YYYY-MM-DD |
| Executive sponsor | [@name] | [Approve] | YYYY-MM-DD |
Revision history
| Version | Date | Author | Change summary |
|---|
| 1.0 | YYYY-MM-DD | [@name] | Example: First policy for QML-3, aligned with Code of Practice Copyright chapter |
| 1.1 | YYYY-MM-DD | [@name] | Example: Added support for a new machine-readable opt-out protocol |
Contents
1. Scope and legal basis
| Item | Value |
|---|
| Legal basis | AI Act Article 53(1)(c); Directive (EU) 2019/790 Articles 3 and 4 |
| Content covered | [Web crawl, licensed datasets, public datasets, user data, synthetic data] |
| Activities covered | [Collection, filtering, training, fine-tuning, evaluation, output generation] |
| Models covered | Example: QML-3 base and QML-3 Instruct by Quillmere Labs |
| Related documents | ai-data/gpai-model-documentation, ai-data/gpai-training-content-summary, ai-data/datasheet-for-datasets |
Key EU rules in short (original summary, check the official texts):
- Directive (EU) 2019/790 Article 3 allows TDM by research organisations
and cultural heritage institutions for scientific research.
- Article 4 allows TDM by anyone on lawfully accessible content, unless
the rightsholder has reserved its rights in an appropriate manner, for
content online in particular by machine-readable means (Article 4(3)).
- AI Act Article 53(1)(c) requires the provider to have a policy to
comply with EU copyright law and to identify and respect those
reservations.
Other jurisdictions, as dated examples: in the United Kingdom, section
29A of the Copyright, Designs and Patents Act 1988 allows copies for
computational analysis for non-commercial research only; secondary
sources reported in March 2026 that the government did not plan a
broader commercial text and data mining exception for now (check
https://www.legislation.gov.uk/ukpga/1988/48/section/29A and current
gov.uk policy before relying on this). In the United States, training is assessed
under fair use (17 U.S.C. section 107); the US Copyright Office released
a pre-publication version of its report Part 3 on generative AI
training on 9 May 2025 (https://www.copyright.gov/ai/).
2. Roles and responsibilities
| Role | Responsibility | Person |
|---|
| Policy owner | Maintains this policy and the public summary | [@name] |
| Crawler engineering lead | Implements sections 3 to 5 in the crawler | [@name] |
| Data governance lead | Approves every dataset before training (section 8) | [@name] |
| Safety lead | Implements output safeguards (section 6) | [@name] |
| Rightsholder relations | Runs the complaints process (section 7) | [@name] |
| General counsel | Oversight, legal review, escalation | [@name] |
3. Lawful access when crawling
| ID | Rule | Implementation | Evidence |
|---|
| LA-01 | Do not circumvent paywalls, logins, subscription models or other effective technological measures | Example: Crawler fetches only pages served without authentication; pages behind login walls are skipped | Crawler config CR-12 |
| LA-02 | Exclude websites recognised in the EU or EEA as persistently and repeatedly infringing on a commercial scale | Example: Blocklist loaded from the EU list once published, plus court orders tracked by legal | Blocklist BL-03, updated [frequency] |
| LA-03 | Do not use content obtained from known pirate sources from any channel | Example: Dataset intake checks source against BL-03 | Intake checklist |
4. Identifying and honouring rights reservations
| ID | Signal | Supported since | Behaviour |
|---|
| RR-01 | robots.txt per RFC 9309, user agent token for our crawler | YYYY-MM-DD | Example: Disallow for our token or for all agents means no fetch and no use for training |
| RR-02 | robots.txt read at crawl time and re-read at least [every 24 hours] | YYYY-MM-DD | Example: A later disallow removes the domain from the next training data snapshot |
| RR-03 | [Other machine-readable protocol, for example a TDM reservation protocol or metadata standard] | YYYY-MM-DD | [Behaviour] |
| RR-04 | Asset-level metadata in files (for example embedded rights metadata) | YYYY-MM-DD | [Behaviour] |
| RR-05 | Reservations communicated by rightsholders through section 7 | YYYY-MM-DD | Added to the exclusion list within [period] |
Decision record for a new protocol:
| Protocol | Adoption evidence | Decision | Date |
|---|
| [Name] | [Standardisation status, adoption by rightsholders] | [Support / Monitor] | YYYY-MM-DD |
Pitfall to avoid: honouring robots.txt only at first crawl. A
reservation added later should stop further use of newly crawled
copies; record how older snapshots are treated and why.
5. Crawler transparency
| Item | Value |
|---|
| Crawler names and user agent tokens | Example: QMCrawl/1.0, token "QMCrawl" |
| IP ranges or verification method | [Published list URL] |
| Purpose of each crawler | Example: Training data collection only; separate from search or retrieval agents |
| robots.txt features honoured | [Disallow, Allow, user agent groups, crawl delay if supported] |
| Other protocols honoured | [List from section 4] |
| Update notifications | Example: RSS feed and changelog page |
| Published at | [URL] |
6. Mitigating infringing outputs
| ID | Safeguard | Description | Test evidence |
|---|
| OUT-01 (Example) | Training data deduplication | Near-duplicate removal reduces memorisation of repeated texts | Dedup report DR-04 |
| OUT-02 (Example) | Regurgitation evaluation | Prompts built from held-out copyrighted samples; long verbatim matches flagged | Eval EV-21 |
| OUT-03 (Example) | Output filter | Blocks long verbatim reproduction of known protected texts such as song lyrics | Filter spec FS-02 |
| OUT-04 | Acceptable use policy clause | Users may not use the model to infringe copyright | [AUP URL, clause] |
| OUT-05 | Downstream documentation | Integrators told about the prohibition and the safeguards | ai-data/gpai-model-documentation |
7. Point of contact and complaints
| Item | Value |
|---|
| Point of contact | [Web form URL, email address] |
| Who may complain | Rightsholders, their representatives, collective management organisations |
| Information requested | Identity, work concerned, evidence of rights, the concern (training use, ignored reservation, output) |
| Acknowledgement target | [For example 5 working days] |
| Decision target | [For example 30 days] |
| Possible outcomes | Reservation added; content removed from future training data; output filter updated; complaint declined with reasons |
| Escalation | [@general-counsel] |
| ID | Received | Complainant type | Issue | Outcome | Closed |
|---|
| CMP-001 (Example) | YYYY-MM-DD | Publisher | robots.txt disallow added before crawl date was not honoured | Domain removed from future data snapshots; crawler bug fixed | YYYY-MM-DD |
| CMP-002 (Example) | YYYY-MM-DD | Collective management organisation | Model output reproduced lyrics | Output filter OUT-03 extended | YYYY-MM-DD |
8. Other data sources
| Source class | Copyright basis | Check before use |
|---|
| Licensed datasets | Licence agreement | Licence permits AI training in the territories concerned |
| Public datasets | Dataset licence and how it was collected | Collection respected reservations; no pirate sources |
| User data | Terms of service and consent | Training use disclosed and permitted |
| Synthetic data | Generated by our or third-party models | Upstream model terms allow it |
| Public domain | Term of protection expired or no protection | Jurisdiction checked |
9. Public summary
[Example: Quillmere Labs trains its QML models only on content it can
access lawfully. Our crawler QMCrawl follows robots.txt and [other
protocols], never bypasses paywalls, and skips sites recognised as
infringing on a commercial scale. We use safeguards to limit
reproduction of protected content in outputs, and our terms prohibit
infringing use. Rightsholders can contact us at [URL]. Crawler details
and updates are published at [URL].]
10. Monitoring and review
| Item | Value |
|---|
| Scheduled review | [Every 12 months] |
| Triggers | New model release, new protocol adoption, EU list of infringing sites published or updated, court ruling, complaint trend |
| Metrics | Complaints received and closed, median days to decision, share of domains with reservations, regurgitation eval results |
| Report to | [Executive sponsor, board committee] |
11. Glossary and references
| Term | Meaning |
|---|
| TDM | Text and data mining: automated analysis of text and data to generate information |
| Rights reservation | A rightsholder's statement that TDM of its content is not permitted, under Article 4(3) of Directive (EU) 2019/790 |
| robots.txt | A file that tells crawlers which paths they may fetch, standardised in RFC 9309 |
| GPAI model | General-purpose AI model as defined in AI Act Article 3(63) |
| Code of Practice | The General-Purpose AI Code of Practice, a voluntary tool under AI Act Article 56 |
References (check the current versions):
- Regulation (EU) 2024/1689 (AI Act), Articles 53, 56, 111(3), 113: https://eur-lex.europa.eu/eli/reg/2024/1689/oj ; amended by Regulation (EU) 2026/1744: http://data.europa.eu/eli/reg/2026/1744/oj
- Directive (EU) 2019/790 on copyright in the Digital Single Market, Articles 3 and 4: https://eur-lex.europa.eu/eli/dir/2019/790/oj
- General-Purpose AI Code of Practice, Copyright chapter, published 10 July 2025: https://digital-strategy.ec.europa.eu/en/policies/contents-code-gpai
- IETF RFC 9309, Robots Exclusion Protocol: https://www.rfc-editor.org/rfc/rfc9309
- Related templates: ai-data/gpai-model-documentation, ai-data/gpai-training-content-summary, ai-data/datasheet-for-datasets, ai-data/ai-system-inventory, ai-data/frontier-safety-framework
Standard and licence note
Based on Regulation (EU) 2024/1689 (the Artificial Intelligence Act) as
amended by Regulation (EU) 2026/1744, Article 53(1)(c), Directive (EU)
2019/790 Article 4, and the Copyright chapter of the General-Purpose AI
Code of Practice published by the European Commission on 10 July 2025.
EU legislative texts are free to reuse; the Code's measures are
summarised in original wording and no text is reproduced. Check the
consolidated text on EUR-Lex, the current Code and Commission guidance
before relying on this policy. All organisations, crawlers and figures
in examples are fictional. This is a documentation format, not legal
advice. Template licence: MIT.