Guest post by Christine Daoutis (UCL), Alex Fenlon (University of Birmingham) and Erica Levi (Coventry University).

This blog post is part of a collaborative series between the UCL Office for Open Science and Scholarship and the UCL Copyright team exploring important aspects of copyright and its implications for open research and scholarship. 

An artist’s illustration of AI by Tim West. Photo by Google DeepMind from Pexels.

A previous post outlined copyright-related questions when creating GenAI materials—questions related to ownership, protection/originality, and infringement when using GenAI. The post discussed how answers to these questions are not straightforward, largely depend on what is at stake and for whom, and are constantly shaped by court cases as they develop.

What does this uncertainty mean for students, academics, and researchers who use GenAI and, crucially, for those in roles that support them? To what extent does GenAI create new challenges, and to what extent are these uncertainties inherent in working with copyright? How can we draw on existing expertise to support and educate on using GenAI, and what new skills do we need to develop?

In this post, we summarise a discussion we led as part of our workshop for library and research support professionals at the Research Libraries UK (RLUK) annual conference in March 2024. This year’s conference title was New Frontiers: The Expanding Scope of the Modern Research Library. Unsurprisingly, when considering the expanding scope of libraries in supporting research, GenAI is one of the first things that comes to mind.

Our 16 workshop participants came from various roles, research institutions, and backgrounds. What they had in common was an appetite to understand and support copyright in the new context of AI, and a collective body of expertise that, as we will see, is very useful when tackling copyright questions in a novel context. The workshop consisted of presentations and small group discussions built around the key themes outlined below.

Perceived Challenges and Opportunities
Does the research library community overall welcome GenAI? It is undoubtedly viewed as a way to make scholarship easier and faster, offering practical solutions—for example, supporting literature reviews or facilitating draft writing by non-English speakers. Beyond that, several participants see an opportunity to experiment, perhaps becoming less risk-averse, and welcome new tools that can make research more efficient in new and unpredictable ways.

However, concerns outweigh the perceived benefits. It was repeatedly mentioned that there is a need for more transparent, reliable, sustainable, and equitable tools before adopting them in research. Crucially, users need to ask themselves what exactly they are doing when using GenAI, their intention, what sources are being used, and how reliable the outputs are.

GenAI’s concerns over copyright were seen as an opportunity to place copyright literacy at the forefront. The need for new guidance is evident, particularly around the use of different tools with varying terms and conditions, and it is also perceived as an opportunity to revive and communicate existing copyright principles in a new light.

Suggested Solutions
One of the main aims of the workshop was to address challenges imposed by GenAI. Participants were very active in putting forward ideas but expressed concerns and frustration. For example, they questioned the feasibility of shaping policy and processes when the tools themselves constantly evolve, when there is very little transparency around the sources used, and when it is challenging to reach agreement even on essential concepts. Debates on whether ‘copying’ is taking place, whether an output is a derivative of a copyrighted work, and even whether an output is protected are bound to limit the guidance we develop.

Drawing from Existing Skills and Expertise
At the same time, it was acknowledged that copyright practitioners already have expertise, guidance, and educational resources relevant to questions about GenAI and copyright. While new guidance and training are necessary, the community can draw from a wealth of resources to tackle questions that arise while using GenAI. Information literacy principles should still apply to GenAI. Perhaps the copyright knowledge and support are already available; what is missing is a thorough understanding of the new technologies, their strengths, and limitations to apply existing knowledge to new scenarios. This is where the need for collaboration arises.

Working Together
To ensure that GenAI is used ethically and creatively, the community needs to work collaboratively—with providers, creators, and users of those tools. By sharing everyday practices, decisions, guidance, and processes will be informed and shaped. It is also important to acknowledge that the onus is not just on the copyright practitioners to understand the tools but also on the developers to make them transparent and reliable. Once the models become more transparent, it should be possible to support researchers better. This is even more crucial in supporting text and data mining (TDM) practices—critical in many research areas—to limit further restrictions following the implementation of AI models.

Magic Changes
With so much excitement around AI, we felt we should ask the group to identify the one magic change that would help remove most of the concerns. Interestingly, the consensus was that clarity around the sources and processes used by GenAI models is essential. How do the models come up with their answers and outputs? Is it possible to have clearer information about the sources’ provenance and the way the models are trained, and can this inform how authorship is established? And what criteria should be put in place to ensure the models are controlled and reliable?

This brings the matter back to the need for GenAI models to be regulated—a challenging but necessary magic change that would help us develop our processes and guidance with much more confidence.

Concluding Remarks
While the community of practitioners waits for decisions and regulations that will frame their approach, it is within their power to continue to support copyright literacy, referring to new and exciting GenAI cases. Not only do those add interest, but they also highlight an old truth about copyright, namely, that copyright-related decisions always come with a degree of uncertainty, risk, and awareness of conflicting interests.

About the authors 

Christine Daoutis is the UCL Copyright Support Officer at UCL. Christine provides support, advice and training on copyright as it applies to learning, teaching and research activities, with a focus on open science practices. Resources created by Christine include the UCL Copyright Essentials tutorial and the UCL Copyright and Your Teaching online tutorial.

Alex Fenlon is the Head of Copyright and Licensing within Libraries and Learning Resources at the University of Birmingham. Alex and his team provide advice and guidance on copyright matters, including text, data mining, and AI, to ensure that all law and practice are understood by all.

Erica Levi is the Digital repository and Copyright Lead at Coventry University. Erica has created various resources to increase awareness of copyright law and open access through gamification. Her resources are available on her website.

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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.

Photo by Google DeepMind.

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.

Image Credit: Kris Kashtanova using Midjourney AI, Public domain, via Wikimedia Commons.

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.

Get involved!

The UCL Office for Open Science and Scholarship invites you to contribute to the open science and scholarship movement. Stay connected for updates, events, and opportunities. Follow us on X, formerly Twitter, LinkedIn, and join our mailing list to be part of the conversation!

Guest post by Christine Daoutis, Copyright Support Officer

Welcome to the inaugural blog post of a collaborative series between the UCL Office for Open Science and Scholarship and the UCL Copyright team. In this series, we will explore important aspects of copyright and its implications for open research and scholarship.

Research ideas, projects, and their outcomes often involve using and producing materials that may be protected by copyright. Copyright protects a range of creative works, whether we are talking about a couple of notes in a notebook, a draft thesis chapter, the rough write-up of a data, a full monograph and the content of this very blog. While a basic knowledge of copyright is essential, particularly to stay within the law, there is much more to copyright than compliance. Understanding certain aspects of copyright can help you use copyright materials with more confidence, make use of your own rights and overall, enhance the openness of your research.

Image attribution: Patrick Hochstenbach, 2014. Available under https://creativecommons.org/licenses/by/4.0/

This first post in our series is dedicated to exploring common questions that arise during research projects. In future posts, we will explore some of these questions further, providing guidance, linking to new resources, and signposting relevant workshops. Copyright-related enquiries often arise in the following areas:

Reusing other people’s materials: How do you GET permission to reuse someone else’s images, figures, software, questionnaires, or research data? Do you always need permission? Is use for ‘non-commercial, research’ purposes always permitted, or are there other factors to consider? How do licenses work, and what can you do when a license does not cover your use? It’s easy to be overconfident when using others’ materials, for example, by assuming that images found on the internet can be reused without permission. It’s equally easy to be too cautious, ending up not making use of valuable resources for fear of infringing someone’s rights. Understanding permissions, licenses, and copyright exceptions – what may be within your rights to do as a user – can help you.

Disseminating your research throughout the research cycle: There are open access options for your publications and theses, supporting access to and often, reuse of your work. How do you license your work for reuse? What do the different licenses mean, and which one is most suitable? What about materials produced early on in your research: study preregistrations, research data, preprints? How can you make data FAIR through licensing? What do you need to consider when making software and other materials open source?

Is your work protected in the first place? Documents, images, video and other materials are usually protected by copyright. Facts are not. For a work to be protected it needs to be ‘original’. What does ‘original’ mean in this context? Are data protected by copyright? What other rights may apply to a work?

Who owns your research? We are raising questions about licensing and disseminating your research, but is it yours to license? What does the law say, and what is the default position for staff and students at UCL? How do contracts, including publisher copyright transfer agreements and data sharing agreements, affect how you can share your research?

‘Text and data mining’. Many research projects involve computational analysis of large amounts of data. This involves copying and processing materials protected by copyright, and often publishing the outcomes of this analysis. In which cases is this lawful? How do licences permit you to do, exactly, and what can you do under exceptions to copyright? How are your text and data mining activities limited if you are collaborating with others, across institutions and countries?

The use of AI. Speaking of accessing large amounts of data, what is the current situation on intellectual property and generative AI? What do you need to know about legal implications where use of AI is involved?

These questions are not here to overwhelm you but to highlight areas where we can offer you support, training, and opportunities for discussion. To know more:

• Follow future posts on this blog.
• Register for an upcoming copyright session. If you cannot make those dates, please contact copyright@ucl.ac.uk to arrange a bespoke session.
• Visit our training and resources page for more ideas and links to online tutorials.
• Have a head start: visit our guidance on copyright and publications, copyright and research data and look out for upcoming guidance on text and data mining.

Get involved!

The UCL Office for Open Science and Scholarship invites you to contribute to the open science and scholarship movement. Stay connected for updates, events, and opportunities. Follow us on X, formerly Twitter, LinkedIn, and join our mailing list to be part of the conversation!