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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