We are approaching the end of International Open Access Week, and we have been enjoying a series of interesting insights and discussions across UCL!  Earlier this week, we explored the balance between collaboration and commercialisation, highlighted the important work of Citizen Science initiatives and the growing significance of open access textbooks.

Today, Christine Daoutis, UCL Copyright Support Officer, will build on our ongoing series about copyright and open science, focusing on how we can ensure free and open access to knowledge in the age of AI, by addressing copyright challenges, advocating for rights retention policies, and discussing secondary publication rights that benefit both researchers and the public.

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Open Access Week 2024 builds on last year’s theme, Community over Commercialisation, aiming not only to continue discussions but to take meaningful action that prioritises the interests of the scholarly community and the public. This post focuses on copyright-related issues that, when addressed by both individual researchers and through institutional, funder, and legal reforms, can help create more sustainable and equitable access to knowledge.

Retaining author rights

Broadly speaking, rights retention means that authors of scholarly publications avoid the traditional practice of signing away their rights to publishers, typically done through a copyright transfer agreement or exclusive licence. Instead, as an author, you retain at least some rights that allow you to share and reuse your own research as openly as possible. For example, you could post your work in an open access repository, share it on academic networks, reuse it in your teaching, and incorporate it into other works like your thesis.

Many funders and institutions have specific rights retention policies that address related legal issues. If such a policy applies, and publishers are informed in advance, authors typically need to retain rights and apply an open licence (usually CC BY) to the accepted manuscript at the point of submission.

Rights retention ensures that your research can be made open access without relying on unsustainable pay-to-publish models, and without facing delays or restrictions from publishers’ web posting policies. Importantly, rights retention is not limited to published research—it can be applied to preprints, data, protocols, and other outputs throughout the research process.

Secondary Publication Rights (SPRs)

Secondary publication rights (SPRs) refer to legislation that allows publicly funded research to be published in an open access repository or elsewhere, at the same time as its primary publication in academic journals. Some European countries already have SPRs, as highlighted by the Knowledge Rights 21 study conducted by LIBER, and LIBER advocates for #ZeroEmbargo on publicly funded scientific publications. There are ongoing calls to harmonise and optimise these rights across countries, ensuring that the version of record becomes immediately available upon publication, overriding contractual restrictions imposed by publishers.

SPRs can apply to different types of research output and are meant to complement rights retention policies. However, introducing SPRs depends on copyright reform, which is not an action individual researchers can take themselves, though it’s still useful to be aware of developments in this area.

Artificial Intelligence and your rights

The rise of Generative AI (GenAI) has introduced broader issues affecting researchers, both as users and as authors of copyrighted works. These include:

• Clauses in subscription agreements that seek to prevent researchers from using resources their institution has subscribed to for AI-related purposes.
• Publishers forming agreements with AI companies to share content from journal articles and books for AI training purposes, often without clear communication to authors. A recent deal between Taylor & Francis and Microsoft for $10 million has raised concerns among scholars about how their research will be used by AI tools. In some cases, authors are given the option to opt in, as seen with Cambridge Press.
• For works already licensed for reuse, such as articles under a CC BY licence or those used under copyright exceptions, questions arise about how the work will be reused, for what purposes, and how it will be attributed.

While including published research in AI training should help improve the accuracy of models and reduce bias, researchers should have enough information to understand and decide how their work is reused. Creative Commons is exploring ‘preference signals’ for authors of CC-licensed works to address this issue.

The key issue is that transferring your copyright or exclusive rights to a publisher restricts what you can do with your own work and allows the publisher to reuse your work in ways beyond your control, including training AI models.

Using Copyright exceptions in research

UK copyright law includes exceptions (known as ‘permitted acts’) for non-commercial research, private study, criticism, review, quotation, and illustration for instruction. As a researcher, you can rely on these exceptions as long as your use qualifies as ‘fair dealing’, as previously discussed in a blog post during Fair Dealing Week. Text and data mining for non-commercial research is also covered by an exception, allowing researchers to download and analyse large amounts of data to which they have lawful access.

Relying on copyright exceptions involves evaluating your purpose and, for some exceptions, making a decision around what is ‘fair’. This also involves some assessment of risk. Understanding copyright exceptions helps you exercise your rights as users of knowledge and make confident assessments as to whether and when a copyright exception is likely to apply, and when permission is necessary. [see links for UK legislation at the end of this article]

Engage with copyright at UCL

The conversations sparked during Open Access Week continue throughout the year at UCL as part of ongoing copyright support and education. To engage further with these issues, you can:

• Add your voice to how copyright literacy is shaped at UCL. Provide feedback on the draft UCL copyright literacy strategy and consider joining the new UCL copyright literacy community.
• Attend a copyright training session or email copyright@ucl.ac.uk to arrange a bespoke session. The newly introduced ‘Copyright, Open Science and Creativity’ game is a fun way of engaging with current debates and learning about recent developments. More dates for this will be released soon.
• Follow copyright news on the Open@UCL blog and the Copyright blog.

Useful Legislation

• Copyright, Designs and Patents Act 1988, Section 29: Research and Private Study
• Copyright, Designs and Patents Act 1988, Section 29A: Text and Data Mining for Non-Commercial Research
• Copyright, Designs and Patents Act 1988, Section 30: Text and Data Mining
• Copyright, Designs and Patents Act 1988, Section 32: Copying for non-commercial research

Guest post by Christine Daoutis, UCL Copyright Support Officer. 

This the third blog post of the collaborative series between the UCL Office for Open Science and Scholarship and the UCL Copyright team. Here, we continue our exploration of important aspects of copyright and its implications for open research and scholarship.

In a previous post we introduced questions that arise when using and creating materials protected by copyright. What options are available to you if you want to reuse others’ work (e.g. articles, theses, images, film, code) in your research? And what do you need to consider before you share your own research with others? Issues around copyright protection, permissions, exceptions, licences, and ownership need to be examined when creating new works and including others’ materials. These questions are also relevant when we think about works that are created with the use of GenAI tools, such as ChatGPT. However, with the use of these technologies still being relatively new and the legal aspects being shaped as we speak, answers are not always straightforward.

GenAI Training Data: GenAI models are trained on a large number of materials, usually protected by copyright (unless copyright has expired or been waived). Does this mean AI companies are infringing copyright by using these materials? How would copyright exceptions and fair dealing/fair use apply in different countries? How would licence terms – including the terms of open licences – be respected? Answers will come both from legislation and codes of practice introduced by governments and regulatory bodies (such as the EU AI Act) and from the outcomes of court cases (see, for example, Getty Images vs Stability AI, the Authors’ Guild against OpenAI and Microsoft.

User Prompts: The prompts a user provides to the model (instructions, text, images) may also be protected. You should also consider whether the prompts you enter include any confidential/commercially sensitive information that should not be shared. Please see UCL’s IP policy for guidance on this.

AI-Generated Work: Is the AI-generated work an original work protected by copyright? Is it a derivative of other original works, and therefore, possibly infringing? If it is protected, who owns the copyright? The answer to this will vary by case and jurisdiction. In the US, a court ruled that AI-generated images in a comic book were not protected, although the whole comic book and story were. In China, it was ruled that images generated with the use of GenAI tools would be protected, with the owner being the person who provided the prompts. The UK’s CDPA (9.3) states that ‘in the case of a literary, dramatic, musical or artistic work which is computer-generated, the author shall be taken to be the person by whom the arrangements necessary for the creation of the work are undertaken’.

In short, GenAI raises questions about what constitutes an original work, what constitutes infringement, how copyright exceptions and fair dealing/fair use are applied, and how authorship is established. While these questions are still being shaped, here are three things you can do:

1. Consider any limitations in using GenAI besides copyright (e.g., confidentiality, biases, publishers’ policies). See UCL’s Generative AI hub for guidance.
2. Be transparent about how you use GenAI. See UCL Library guidance on acknowledging the use of AI and referencing AI.
3. If you have any copyright-related questions on the use of GenAI, contact the copyright support service.

 While GenAI has opened up more questions than answers around copyright, it also offers an opportunity to think about copyright critically. Stay connected with us for Part 2 of this blog post, which will discuss how new technologies, including GenAI, are changing our understanding of copyright. We look forward to continuing this important conversation with you.

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