Information on AI and copyright for UCL Press authors

Open Access works are freely available for access, sharing and reuse under the terms of the Creative Commons licence. See Creative Commons licensing and attribution above.

Under the terms of Creative Commons licences, reuse is permitted, provided that attribution is made for direct citation, though there are limitations to the ways in which works can be attributed in Gen AI outputs (see below). This will vary depending on which licence is chosen. A CC BY-NC (non-commercial) licence should prevent an AI company using data for any commercial purpose; however, text and data mining exceptions may apply in some regions. 

Increasingly, AI tools include a link to a source when generating responses to prompts, although this often requires a specific request. We’ve seen examples of links to UCL Press books on the UCL Press website, author websites and on distributor platforms. While direct attribution would be required for text extracts, Gen AI outputs don’t generally reproduce texts verbatim; rather they use works to train LLMs, which then provide new information and responses to prompts by synthesising huge datasets.  

As above, this can be challenging when answers to prompts provide a synthesis of large amounts of information rather than direct quotes. 

Since UCL Press publications are already Open Access, which means that access, sharing and reuse is permitted under the terms of a Creative Commons licence, it’s unlikely that AI companies will seek such arrangements. Their main interest is gaining access to large volumes of copyright materials that are otherwise inaccessible. 

A government consultation on AI and copyright was undertaken in early 2025 and the outcome of this is awaited. The government proposals recommend the implementation of an ‘opt out’ system in which copyright owners would need to restrict the use of their works by AI companies. Current copyright legislation requires permission to be granted by copyright owners before their works may be used and this is felt to be entirely adequate as a protection. The proposals would represent a significant change if implemented and would put the onus on the copyright owner to opt out. There are widespread concerns about the implications of such a change in the legislation among the creative industries. 

EU: The EU directive on copyright and related rights in the Digital Single Market (Directive (EU) 2019/790) includes two exceptions that allow Text and Data Mining (TDM). Article 3 of the Directive allows research organisations to carry out text and data mining for the purposes of scientific research, provided that they have lawful access (which includes open access) to the source materials. Article 4 allows text and data mining of lawfully accessed works for any purpose, unless rights holders have expressly reserved their rights (‘opted out’ of the exception): in this case, the works cannot be reproduced under the exception. . 

While some questions remain whether the TDM exceptions apply to training AI models, the EU AI Act (article 53) mentions TDM in the context of general-purpose AI, indicating that AI providers must respect rights reservation when relying on the exception. The Kneschke vs. LAION case in Germany is also the first case to clarify the application of the TDM exceptions to AI training (see an extensive analysis of this case on the European law blog).  

USA: ‘Fair use’ is a US legal doctrine that allows the use of copyright-protected works without permission if certain criteria apply.  These are: the purpose and character of the use, the nature of the work being used, the amount and substantiality of the work being used, and the effect of the use on the market of the work. Whether use of copyright-protected works is ‘fair use’ will be informed by the outcomes of cases that are still in progress. However, existing precedents involving machine learning (if not AI training) lend support to the argument that such activities should be considered ‘fair use’. For a relevant discussion see the Association of Research Libraries blog.

The UCL Press website currently has Robots.txt in place, which signals to AI bots that we don’t want our website to be crawled. This signals a preference but doesn’t prevent AI bots from crawling the website. UCL Press publications are hosted on third-party platforms and the measures they have in place to prevent AI bots will vary – see below. 

Solutions such as Robots.txt can be installed on websites to signal to AI bots that website owners don’t want their content to be crawled; however, this signals a preference rather than providing a block. Newer solutions, such as Cloudflare, claim to be able to prevent AI bots. UCL Press content is mainly hosted on third-party platforms in order to ensure widespread dissemination. Those will have different mechanisms in place that UCL Press doesn’t control. The platforms hosting Open Access content may not feel that a complete block is necessary or desirable and that the content should be open to use under the terms of the licence.