AI for Knowledge

AI for Knowledge explores the question of whether artificial intelligence (AI) is transforming knowledge access and creation for the good. AI is accelerating our access to knowledge through search, recommendation, summarisation, translation and a proliferating range of tools with AI built in. Generative AI is further changing how we use and create information at home and in the workplace. Yet AI also has a dark side with hallucination, bias and lack of explainability, as well as potential for harmful impacts on social equity and the environment.

The book investigates how AI will impact everyday knowledge discovery, understanding and creation. It considers both the positive benefits and the many informational and ethical challenges, including the impact on our wider information culture. It then weighs up the impact on scholarship, in science, social science and the humanities and including the processes of scholarly communication. It explains the role of libraries and archives and how they could be enhanced using AI. It concludes by showing how governments can regulate AI to ensure social benefit and outlines what we as individuals need to know about AI.

The book helps the reader see through some of the AI hype to understand much more clearly what are the issues around the impact of AI on knowledge access and creation, including the implications for environmental sustainability and the power of Big Tech. What emerges is a nuanced picture of potential benefit and risk, especially when we consider the experiences of those with less privileged access to the digital, particularly those outside the Global North.

Andrew Cox, PhD, is Senior Lecturer at the Information School, University of Sheffield, United Kingdom. His interests include the impact of datafication and AI on knowledge creation and use.

Mike Thelwall, PhD, is Professor of Data Science at the Information School, University of Sheffield, United Kingdom. His interests include applications of artificial intelligence for research evaluation.

AI for Everything

Artificial intelligence (AI) is all around us. From driverless cars to game winning computers to fraud protection, AI is already involved in many aspects of life, and its impact will only continue to grow in future. Many of the world’s most valuable companies are investing heavily in AI research and development, and not a day goes by without news of cutting-edge breakthroughs in AI and robotics.

The AI for Everything series explores the role of AI in contemporary life, from cars and aircraft to medicine, education, fashion and beyond. Concise and accessible, each book is written by an expert in the field and will bring the study and reality of AI to a broad readership including interested professionals, students, researchers, and lay readers.

AI for Finance

Edward P. K. Tsang

AI for Communication

David J. Gunkel

AI for Scientific Discovery

Janna Hastings

AI for Peace

Branka Panic

Paige Arthur

AI for Knowledge

Andrew Cox and Mike Thelwall

For more information about this series please visit: www.routledge.com/AI-for-Everything/book-series/AIFE

AI for Knowledge

Andrew Cox and Mike Thelwall

First edition published 2025

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© 2025 Andrew Cox and Mike Thelwall

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ISBN: 9781032898926 (hbk)

ISBN: 9781032874241 (pbk)

ISBN: 9781003545163 (ebk)

DOI: 10.1201/9781003545163

Typeset in Sabon

by Newgen Publishing UK

Contents

1 Introduction

2 AI and everyday knowledge

3 AI and research

4 Generative AI and research practice

5 AI and scholarly communication: Peer review

6 Libraries and archives

7 Ensuring the benefits of AI are shared fairly

8 Conclusion

Index

Chapter 1Introduction

DOI: 10.1201/9781003545163-1

AI and knowledge access and creation

The question that this book explores is whether artificial intelligence (AI) is transforming knowledge access and creation in a significantly positive, equitable, inclusive and sustainable way.

This question has become relevant because of changes such as the increasing depth and reach of search in a digital world, the power of recommendation and adaptivity to give us the information we need in the form we want it and when we want it and the increasing ability of AI-based translation to give us access to knowledge in other languages. These have had and continue to have significant impacts.

But a further leap has happened with generative AI appearing to significantly accelerate these benefits by helping us discover information, understand it and supporting many aspects of knowledge creation. Writing in the historical moment when the impact of generative AI is being worked through, it is important to ask how AI is impacting knowledge.

Generative AI seems to be a disruptive technology. The following quotes express something of the surprise, excitement and fear we feel about it.

AI is sexy, AI is cool. AI is entrenching inequality, upending the job market, and wrecking education. AI is a theme-park ride, AI is a magic trick. AI is our final invention, AI is a moral obligation. AI is the buzzword of the decade, AI is marketing jargon from 1955. AI is humanlike, AI is alien. AI is super-smart and as dumb as dirt. The AI boom will boost the economy, the AI bubble is about to burst. AI will increase abundance and empower humanity to maximally flourish in the universe. AI will kill us all.

Ioneda, 2024

During these 365 days, ChatGPT has broadened the public perception of AI, captured imaginations, attracted critics, and stoked existential angst. It emboldened and reoriented Microsoft, made Google dance, spurred fears of AGI [artificial general intelligence] taking over the world, captivated world leaders, prompted attempts at government regulation, helped add words to dictionaries, inspired conferences and copycats, led to a crisis for educators, hyper-charged automated defamation, embarrassed lawyers by hallucinating, prompted lawsuits over training data, and much more.

Edwards, 2023

AI is the defining technology of our generation.

UK government, 2024

Generative AI appears to be changing many aspects of our world, not least for important questions about knowledge access and creation. But the question of how deep and significant the change it is making is a very large one. It implies considering multiple contexts where we access and create knowledge, be that in our everyday lives, as consumers, workers, learners and citizens or in the realms of science and scholarship where new knowledge is created. It must take into account global experiences, not just the perspective of a privileged minority in the Global North. It also implies trying to reflect on the impacts of technologies over a period and guess at its impacts in the near future.

This short book cannot definitively answer all aspects of this question, but it can do useful work to define the scope of the question and point to some of the emerging evidence.

An information science perspective

The authors’ belief in the importance of AI for knowledge access and creation is rooted in the values and theories of Information Science as an academic discipline and professional practice.

Information Science is the study of the creation, organisation and use of information and knowledge. It is a distinct discipline with links to computer science and social sciences such as sociology and media studies. Its roots are in institutions such as libraries and archives as gateways to knowledge. But in what has been called the information or knowledge society, the concerns of Information Science extend greatly beyond these specific locations, to how we find, evaluate, use and create knowledge in our everyday lives, in workplaces, as consumers and as citizens. There is also a strong interest in how knowledge is created and used within academia.

There are many expressions of the values and ethical commitments of Information Science (IFLA, 2012). These centre on the rights to access knowledge and freedom of expression. The values of Information Science imply a human right to actively search for, find and make sense of information and knowledge, and also a right to create, share and publish ideas.

Information Science is based on the belief that the right of access to knowledge is core to human flourishing and critical to societies. It is important for informing ourselves about the everyday life choices we make and the decisions we make as consumers, parents and employees. It is important to our health-related choices. It is central to our role as social media consumers and creators. Access to knowledge and freedom of expression are vital to learning. We must be able to explore published knowledge, ask questions about it and express our own constructions on them to learn. The right to access knowledge and express ideas are also central to the production and circulation of scholarly knowledge to the progress scientific disciplines. Freedom of access to knowledge and expression are essential to all citizens in democracies. Democracy cannot work if these values are significantly compromised. Access to knowledge is also central to our heritage and identities. We need access to the record of experiences of the past lives and communities we identify with. The question posed in this book is how does AI impact our knowledge access and creation across these contexts?

The stress Information Science places on knowledge is inclusive. It values the diversity of human knowledge. Knowledge might most obviously be in the form of written words in books and other publications, or on websites and in other digital forms. Knowledge from the sciences has a central place in Western culture. However, knowledge explored in other disciplines, such as the humanities, including history and literature, is also important. Knowledge is often created through imaginative means, and in non-linear ways. Knowledge can be represented in visual images rather than text. Information Science also places value on the small acts of creation of knowledge in blogs and forums. Information Science pays respect to all knowledge systems where knowledge might be transmitted orally or by demonstration or through daily practices, such as indigenous knowledge systems. The question becomes how does AI touch this range of knowledge systems and the diversity of knowledge flourishing within them?

The commitment of Information Science to equity in access is also important. People should be able to access knowledge freely regardless of their age, citizenship, political belief, physical or mental ability, gender identity heritage, education, income, migration and asylum-seeking status, marital status, origin, race, religion or sexual orientation (IFLA, 2012). It recognises that there are many preconditions for this that need to be established. Basic literacy is often one. An important concern, with implications for the benefits of AI, are digital inequalities. As information is increasingly digital there are not only many obstacles to equitable access, such as barriers of technology access, bandwidth and cost, but also issues around skills, confidence, time and motivation. Digital inequalities result from and reinforce other inequalities. Those with less access to education, skilled jobs and money are also often digitally excluded, creating a vicious cycle of being underprivileged. There are international digital inequalities too. In trying to think through the implications of AI for knowledge, we want to avoid the trap of focussing only on the experience of the well-off city dweller in the Global North, and remember those in the less-privileged majority world (the world beyond America and Europe, and the “West” where most humans actually live), and the experiences of the older person or the person with disabilities – especially when we recognise the diversity of Global South contexts and experiences of disability.

The question posed in this book also refers to the sustainability of AI’s benefits. Taking the interpretation of sustainability from the UN’s Sustainable Development Goals, it implies a concern with global and social equity concerns, and environmental sustainability as well. Perhaps AI can be used to cope with the climate crisis but it has a substantial environmental impact itself.

Information Science places access to knowledge and the right to free expression as core values, but it must be acknowledged that there are some important qualifications to these as absolute rights in certain circumstances. Many societies recognise that individual authors, organisations and communities can have ownership rights over expressions of knowledge. This includes copyright and intellectual property rights for authors. Without these protections one of the main motivations for authors to publish their ideas would disappear. Information Science also seeks to promote communities’ rights to control traditional cultural expressions, such as stories, songs, rituals, crafts, designs and art. These embody knowledge. Such rights exist in tension with a belief in open access to knowledge for all. It is also concerned with our rights over data that describes ourselves. Information Science recognises a tension between the need for organisations and governments to be transparent, and their right to some confidentiality, such as in decision-making. Similarly, the right to individual privacy cuts against a completely untrammelled right of access to knowledge. These qualifications are important, and as with our access to knowledge, have implications for the working of AI.

Notwithstanding these qualifications, Information Science’s commitment to knowledge access and creation for all is central to how the question posed in this book is scoped. The importance of the rights to access and create knowledge reflect that they are rights continuously under threat. Many governments suppress access to knowledge through unwarranted secrecy, censorship, surveillance, propaganda and even violence. Groups and malicious individuals that hate democracy and inclusion are a source of deep disinformation, information shared to deliberately mislead others. There is also the power of commercial organisations and media organisations to flood channels with marketing content or to select, frame or censor information, and extend commercially motivated surveillance over our behaviour. How such threats can be identified and overcome either by technical means, governance mechanisms or by equipping people with skills of critical understanding, are central questions for Information Science.

Therefore, how AI is impacting these threats to knowledge is an important dimension of the question that is posed in this book.

Information Science also wants to counter our own carelessness around information and common patterns of satisficing behaviour when we simply do not give enough time to consider the accuracy of information, feel overloaded by the sheer quantity of information we encounter or even endanger our own privacy and intellectual property rights through signing online terms and conditions carelessly. Information Science promotes the skills to evaluate information, manage information and data, and create new knowledge.

Thus, this book is rooted in the particular perspective of Information Science and adopts a self-consciously normative stance.

A deFInition of AI

To clarify the question that is posed in this book it is important to explain how the term AI is being used. When we think about technologies, we may think simply about the functions they perform, such as filtering spam or summarising documents. This narrow view is reflected in common discourses around AI that regard it as “just a tool,” implying that it is neutral in itself and the benefits, costs or risks arise just from how we choose to use it. Other common discourses around technologies such as AI contain a large element of technological determinism and solutionism: assumptions that technology drives inevitable change and that it can, by itself solve complex societal problems. We often also hear that AI is the future, as if there was no way that society and individuals can influence its character or resist its use.

In contrast to these solutionist and deterministic discourses, Information Science treats technologies as socio-technical systems. It recognises that technology is created by social actors who build their own interests, values and assumptions into it.

Technology has social implications in terms of often reinforcing or shifting structures of power and inequality. Thus, employers can use technologies for surveillance of staff and intensify control over them. Governments also use technologies of control.

International inequalities can also be exacerbated through how technologies that are designed for one context and thrust into another. Equally there is the possibility of social actors resisting such pressures and using technologies in unexpected and empowering ways. Technology in itself does not determine social change. The functions of tools and systems cannot be separated from the context of their production and implementation, such as the ownership and business models of IT companies. Neither can the underlying technologies be separated out from systems of governance that seek to shape their design and implementation. This book does not treat AI as just a set of functions, rather it also probes into their social creation and consequences.

In particular, it is important in the case of AI to recognise that the AI industry, following the example of search engines, has given mass free access to tools such as ChatGPT in support of their long-term profit seeking goals. There is an important view that must be acknowledged that sees the AI industry as deeply exploitative and extractive (Crawford, 2021). Whereas we might take for granted free access to much AI, we must not ignore the way that knowledge content and data has been extracted to train them and the deep environmental and economic impacts, usually occurring out of sight in the majority world. For some, these aspects are rooted in an industry that is far from inclusive in its workforce, relies on exploiting precarious labour to perform essential tasks and where decision-making is ultimately driven by profit not public good. This book will consider how this perspective shifts the view of the inclusivity, equity and sustainability of the knowledge benefits of AI.

Furthermore, AI is not a simple or single technology to understand or define. It is as much an idea as a specific technology. The term “AI” can be traced back to the 1950s and the idea of thinking machines goes still further back. But the meaning of the concept evolves over time. It is often described as technology performing tasks that normally require human intelligence, but this is a quite vague idea.

A key feature of some, but not all, AI is that it is able to “learn” from data. This means that its outputs can be unexpected even to its creators, and its learnings can include biases and misinformation. Its ability to learn also implies a degree of autonomy.

Current AI and is mostly data driven, implying training AI on masses of data.

The term AI in its current meaning encompasses a range of technologies that themselves appear to be shifting quite rapidly. A helpful starting point is the following definition:

… any software technology with at least one of the following capabilities: perception – including audio, visual, textual, and tactile (e.g., face recognition), decision-making (e.g., medical diagnosis systems), prediction (e.g., weather forecast), automatic knowledge extraction and pattern recognition from data (e.g. discovery of fake news circles in social media), and logical reasoning (e.g., theory development from premises). This new field encompasses a large variety of subfields, including machine learning.

Vinuesa et al., 2020

The quote is useful for the purpose of this book in focussing on what AI accomplishes, rather than underlying technologies such as neural networks or machine learning. The definition is helpful in capturing different types of things current AI can do. The quote also expresses the multiplicity of AI. AI is hard to understand partly because there are many different technologies currently included under that umbrella term. Of particular relevance to this book are a few of the capabilities defined in the quote. The “perception” functions of AI are important to the increasing digitisation of voices, texts, images and so forth. This means that spoken testimonies or commands, handwritten manuscripts or still and moving images can be understood by computers as some form of data. Computers can also produce different forms of output, such as speech.

The concept mentioned in the quote of “Automatic knowledge extraction and pattern recognition from data” is important to allow us to model the meaning of such data and so to navigate this content in different ways. The label “descriptive AI” could be used for this kind of technology.

Of course, this definition comes from before generative AI became popular, and so needs to be updated to include its complex capabilities. Further reinforcing our understanding of AI as a multiple and evolving idea, generative AI such as ChatGPT introduces many new capabilities which are important for accessing and creating knowledge, such as summarising content or writing drafts of text.

In this book, we are going to use the term generative AI, but much of what is being referred to is based on large language models (LLMs). LLMs are mathematical representations of language based on predicting the probability of what is likely to come next in a sequence of words. The way that predictive text tries to complete a word or sentence as you type is an earlier form of this technology. Current generative AI is based on a particular type of neural network called a transformer. These are trained on truly vast amounts of text to enable them to predict appropriate text to follow a simple prompt.

The focus of AI development may well shift significantly again in the next few years, maybe very soon. When we talk about AI we are typically talking about the future. AI is almost always constructed as about a predicted future. This book is located in the current historical moment when we are working through the implications of generative AI, alongside processes of datafication and the growing use of descriptive AI.

Conclusion

In attempting to clarify and answer the question posed in this book, the chapters are organised as follows. Chapter 2 explores how AI is changing access to knowledge in everyday contexts. It also explores the limitations and risks attached to these changes. Chapters 3, 4 and 5 focus more specifically on the impact of AI in transforming scholarly knowledge creation and use. Again, the book is alert to risks and barriers. Chapter 6 looks specifically at how access to knowledge through libraries and archives could be transformed by AI, because they are key access points to knowledge. Chapter 7 considers the kind of governance AI needs to ensure that the benefits are equitable and sustainable. It also considers the kind of AI skills we need as individuals to operate effectively in an information environment strongly shaped by AI.

References

Crawford, K. (2021). Atlas of AI: Power, politics and the planetary costs of artificial intelligence. New Haven: Yale University Press.

Edwards, B. (2023). ChatGPT is one year old. Here’s how it changed the tech world. Ars Technica. https://arstechnica.com/information-technology/2023/11/chatgpt-was-the- spark-that-lit-the-fire-under-generative-ai-one-year-ago-today/.

IFLA. (2012). IFLA Code of Ethics for librarians and other information workers. www.ifla.org/g/faife/professional-codes-of-ethics-for-librarians/.

Ioneda, J. (2024). What is AI? MIT Technology Review.

https://www.technologyreview.com/2024/07/10/1094475/what-is-artificial-intelligence-ai-definitive-guide/.

UK Government. (2024). UK & United States announce partnership on science of AI safety, Press release 2 April 2024, https://www.gov.uk/government/news/uk-united-states-announce-partnership-on-science-of-ai-safety.

Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., ... & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 1–10.

Chapter 2AI and everyday knowledge

DOI: 10.1201/9781003545163-2

This chapter explores how the capabilities of AI may be increasing knowledge access and creation in everyday contexts. Particularly in modern, knowledge societies, access to knowledge can be a critical aspect of daily experience, for example, in terms of informing decision-making about purchases, finding a job or making voting choices. The analysis also needs to capture the problems of knowledge access and creation that AI also has. Weighing up the benefits and costs is central to trying to answer to the question posed in this book.

A simple structure within which to explore the impact of AI on everyday knowledge experiences is around discovery, understanding and creation. We do not want to separate out too crudely these processes which are often interwoven, in complex, iterative and non-linear paths, but for the purposes of exegesis a little simplification is helpful. We can describe some of the ways these experiences are changing, with many already familiar shifts extending to what we know or can anticipate from the impact of generative AI.

Knowledge discovery

Increasingly, in the last two decades AI has led to information being pushed to us as we scroll newsfeeds on our phone and we are exposed to personalised information (including advertising) on the websites we visit. Such information is being filtered based on algorithmic calculations of our interests be that partly based on our individual past behaviour, patterns in groups which we are deemed to be a member of, sender decisions or platform preference. In a similar way, material such as spam is filtered out of our email, with more or less accuracy. Automated moderation on platforms seeks to protect us from unpleasant or simply time wasting material. Equally, content can be prioritised for our attention, such as important emails based on who sent them or their contents. Increasingly, such material could also be personalised to us in terms of representation, based on how we like to receive information. When we actively search for information, the same filtering processes are at work.

Generative AI is shifting our expectations here. Increasingly, information seeking tasks are based on natural language questions in an interactive conversation. We do not have to think of carefully chosen keywords to query a database or search engine, indeed we can participate in a dialogue to clarify our question. It can often be difficult to formulate a question and to find the keywords that work effectively in a particular database, so this makes searching easier.

Generative AI promotes a more interactive way of exploring information, with the computer answering and then responding to follow up questions, even prompting us for further questions we could ask. This has great potential to help us overcome our limited skills with the difficult task of asking our questions precisely. We can expect these features to appear in all search engines. Further, this conversation can be through spoken dialogue or written text. Over time generative AI may learn about us and can adapt its answers to our needs. Critically with generative AI the answers to our search questions are presented in the form of answers rather than just a list of resources, that we are ourselves then need to read and synthesise. The striking difference between a search engine and generative AI is that one gives a list of results we comb through, whereas the other gives a coherent answer to our question. In many contexts this is essentially what we want.

Understanding

AI can be part of the process of understanding information. Generative AI is good at summarising a text to different lengths, or from different perspectives, such as for a particular reading age, or focussing on a specific aspect of the text. Given the great amounts of information we are faced with in everyday life, the ability to summarise it in ways that suit our ways of understanding is tremendous. Generative AI can also help us query a text in a conversational way making the experience of reading more interactive.

AI-based translation is not very central to current discourse around AI but there are exciting possibilities due to access to knowledge in other languages and scripts having become possible. It is a wonderful thought that we can now access a wide range of knowledge in other languages. A few languages dominate online content but they do not represent the diversity of human knowledge.

What Vinuesa et al. (2020) referred to as the perception function of AI promises also to transcribe spoken material such as a video call that was missed, to take minutes at an online meeting, and even to summarise the meeting and identify action points or points of contention. Automated transcription and subtitles could be a great boon for those with a disability, even though it was not originally designed for this.

Generative AI can also be used to analyse information rather than just providing text summaries. This might be to suggest analyses of statistical data in a spreadsheet or find codes and themes in qualitative data, such as open text responses to a marketing survey or service feedback. It can pick out patterns of sentiment in a large amount of text data, perhaps to see whether most contributors like or dislike an idea. Access to these capabilities allow us to increase our understanding more quickly.

These supports to understanding seem likely to continue to expand in the future. This book makes the assumption that AI can become much more reliable and accurate at carrying out such understanding tasks, so that it can improve access to knowledge in significant ways relevant to everyday experience.

Knowledge creation

In terms of knowledge creation, we are already very familiar with auto-suggestion functions that complete sentences as we write a text or email. We increasingly rely on grammar and spell checking to ensure the quality of our writing. Generative AI takes this a step further in supporting us writing up our ideas, perhaps by suggesting an initial structure, drafting text, changing the style or tone of text or checking grammar in a late draft. It can also perform accessibility checks on our writing.

Generative AI is further promising to translate knowledge between types, such as turning a report into a blog post and a written report into a presentation, developing an automated voice-over based on some presentation slides or producing a visualisation based on data. This could be particularly beneficial to certain social groups, such as those who have a sensory disability. Perhaps this will increase the diversity of voices we have access to.

Taken together, these capabilities of AI to support discovery, understanding and expression promise to transform our access to knowledge and our ability to express our understanding. The extent of these changes when compared to relying pen and paper and printed books a couple of decades ago is startling. They seem potentially to do much more than save time for knowledge tasks, by support improvements and even greater creativity, such as by giving access to more knowledge and brainstorming ideas. At this historic moment of generative AI we are glimpsing a further fundamental round of change.

An example: A university student writing their final year project

To articulate these benefits in a less abstract way let us consider how AI might support knowledge access and creation in an undergraduate student’s final year project. Let us say they are undertaking a desk-based study of the social impact of AI.

AI could offer input on the time management for the project with some insight into the student’s own preferred or usual patterns of working and suggesting how the project is broken down and adjusting suggestions over time as the project progressed.

In terms of discovery and understanding, the student would be likely to start by trying to scope out the topic (discovering the range of types of social impact of AI) and use AI to help understand difficult terms (to find different definitions of AI). AI can give them directly an initial definition of core concepts. The AI could help them to find a few key pieces of literature to frame their approach. Perhaps the student would be offered lists of suggestions to select from and be presented with summarisations of wider debates. If they wanted to pursue a particular interpretation of the issues (e.g. a feminist perspective) the AI could be able to suggest the kind of themes that would be central. It could perhaps also suggest an approach through the lens of a particular feminist author.

The student can interact with texts and with careful prompting get summaries and material in a timely way, in a form that met their preferred way of learning, so offering an element of personalisation and adaptivity.

The student might want then to start selecting items to build up a corpus of material from which to write their report. The AI could help thinking of the best search strategy – where to search and what terms to use. The student might well wish to include AI-created summarisations of lectures they have attended and their own notes. It could be inclusive of items in any language.

Having built up a corpus of material, the AI could offer summarisation of a body of literature and ways to interact with it. The student could explore summaries and particular aspects through asking questions. In addition to their own queries to the literature, the AI could suggest relevant questions to ask. The student could access tools to explore how items relate to each other, for example, not only through a network visualisation based on citations but also clustering of concepts.

We would expect the student to read the texts they plan to cite themselves very carefully and critically. The task has been made easier through the previous process of selecting items, but the student should not rely on these summaries. They should add their own notes to the corpus of material AI being worked with.

From here the student is starting to move into the knowledge creation stage. AI could help by suggesting possible structures for the report to present an emerging argument. However, we would surely again expect the student to continue to manually write the essay section by section themselves. Once a first draft is written the AI could offer suggestions on tone and style and the ordering of ideas. It could provide a check of a late draft against the rubric of criteria for the assignment. It could offer a final proof read.

We can imagine this to be a highly effective learning process, compared to the past where a huge amount of time would be spent locating material, with fragmented and limited support from search engines or grammar checkers. A wider range of literature can be engaged with because the AI offers support to navigate with the scale of literature. There are many positive dimensions in the way the AI assists in the process of building understanding and helping to break down a highly complex task. AI help can be highly adaptive to the level of the task (undergraduate final year project) and individual learning preferences. It is interactive, offering choices to the student but also suggesting paths of analysis. Its responses are immediate and potentially adapted to our own need.

However, we may also feel rather uneasy about certain aspects of this imagined process. Obviously, there is the danger that the student could simply use AI to do perform parts of the task for them, such as analyse and write their project for them. But even if this is not the case, defining the border between AI helping and performing essential parts of the task is not easy.

The degree of help being offered by the AI implies a potential loss of agency and growing dependence. How can the use of AI be monitored or declared by the student? Used carefully AI can support student creativity, but at what point does the ideation by the AI block the student’s own creativity? We will also wonder about the impact on the impact on role of teacher and the student’s relationship with the teacher and other students. Interacting with AI seems to be substituting for human interaction.

Could this way of learning end up being less socially connected?

In this case the project writing task has been made efficient and less stressful, but has it removed central processes for learning, such as struggling through the process of finding patterns in diverging literature? In fact, we may need to question whether this type of assignment now is a suitable learning experience or test of understanding.

We also need to ask whether this complex information intensive task is the best test of how AI is impacting everyday knowledge. We could have selected different examples such as finding a job, keeping informed as a citizen or making a major purchase. This is a longer process of monitoring many sources to build up an information picture. Here it feels like it will be an increasingly automated process, especially in the case of a consumer weighing up information for a major purchasing decision. What are the wider impacts of AI on knowledge access and use?

With these concerns in mind we can turn to discuss the limitations and risks of AI in knowledge in more detail. Much has been written about the potential risks of AI in general (UNESCO, 2021) and generative AI in particular (Anwar et al., 2024; Hagendorff, 2024; Slattery et al., 2024) but our focus is specifically in relation to our question about knowledge access and creation.

Limitations and risks

AI recommendation of information, books, or products has been a particular focus for concern with AI interventions (Milano et al., 2020). This therefore provides a good place to start a discussion of the limitations and risks of AI for everyday knowledge creation and discovery.

The problems of recommendation

AI-based recommendations and AI-selected adverts are often simply unhelpful, mistaking our interests, for example because of inaccurate profiling of us based on too narrow data. Information of value to us can therefore be filtered out and items of limited interest pushed towards us.

What is pushed toward us also reflects imbalances in available sources such as mainstream viewpoints or views companies promote. It can be unbalanced also because of information providers’ success at promoting their materials to AI algorithms or platforms’ desire to nudge our behaviour, such as to stay on the platform or make a purchase. The filtering of information is far from neutral or objective. This has important hidden impacts on our autonomy in choice.

The very act of trying to encapsulate us and our complex identities in a simplistic profile can be seen as dehumanising. It could also be considered an invasion of privacy, as well as potentially creating a sense of surveillance. There may be a chilling effect on freedom of speech if we know we are being profiled online. Any sort of surveillance of knowledge activities may subtly undermine our sense that we are free to access knowledge.

An important issue with profiling and recommendation is that it may create filter bubble effects, where information that is unexpected or challenges our assumptions is largely hidden from us so that without realising it we are narrowing the range of knowledge we encounter. This potentially cocoons us with in spaces where we only hear what we expect or want to hear. Of course, this already happens offline to some extent as we choose our friends and interact with those around us, but if online spaces like YouTube appear to be universal but automatically tailor recommendations for us then this can create a false sense of the wider reality.

The impacts of recommendation are all the more insidious in that we are not necessarily aware that it is happening or don’t know how we are profiled. It is as much the invisibility and opacity of such filtering by AI systems hidden in the infrastructure of knowledge platforms that constitutes a threat to our access and creation of knowledge. Developers may see their filtering algorithms as a commercial secret, they certainly do not make much effort to make them transparent. Individuals need what has been called algorithmic literacy to understand this and maintain their own agency (Dogruel et al., 2022).

These problems have been become familiar in the last decade. Studies suggest that while aware of these issues, most people accept a trade off with the value of functions such as filtering and with a sense that it’s a necessary evil in return for free services. A further factor in acceptance is that the working of such processes is largely invisible to the user. However, some have argued that the creeping extension of AI in the infrastructures of information on platforms we are largely unaware of is the most significant threat to access to everyday knowledge. Currently, when we use generative AI we know we are doing so. There is a large discourse around working AI almost as a collaborative partner. But far more problematic may be the continued extension of AI to shape our behaviour and effectively make decisions for us through processes like recommendation and filtering. Commercially driven AI may aim to construct us as happy, uncritical consumers. In a sense what the purpose current AI is serving is to train us to think and behave towards AI in particular types of ways.

Information accuracy, bias and our information culture

Generative AI brings with it many new information accuracy and bias challenges. Generative AI chatbots such as ChatGPT are not search engines, and output usually plausible answers without a process of checking the accuracy of statements they produce, and sometimes without being able to point to a source of their information even if requested. Indeed, the interfaces of most such chatbots acknowledge this problem and remind the user to check the accuracy of their outputs. “ChatGPT can make mistakes. Check important info.” Generative AI can be out of date, make up or “hallucinate” information, fail to give sources for its answers or even make up sources. The fact that they give a different answer to the same question on other occasions makes it hard to verify and impossible to cite as a source. Their failure to give their sources offers a poor model of information sharing, breaking the chain of knowledge that allows us to check back to the origins of ideas.

The level of inaccuracy in answers from uninformed use is quite striking. For example, the guardrails of politeness on many generative AI chatbots mean that they will agree with false information if it is suggested to be true by the inquirer. We have to actively state in our prompts that we want an entirely honest answer.

The importance of absolute accuracy varies from context to context and task to task. If generative AI is being used in an educational context there is probably some tolerance of inaccuracy. It is very different in a professional health or legal context where highly reliable information may be essential.

Discriminatory bias is an even more insidious issue in generative AI chatbot answers. Many studies have found all sorts of political, gender, racial and religious bias in the outputs of generative AI (e.g. Motoki et al., 2024; Deshpande et al., 2023). This both propagates stereotypes and acts to discourage stigmatised groups from using it. Bias in AI arises from patterns in training data and from how this data is curated. As generative AI is trained partly on the mass of internet content it is likely to reproduce many current assumptions. The generative AI services have made efforts to eliminate the most egregious errors through manual filtering. But this balancing is itself premised on a particular world view. AI is therefore far from neutral. Yet AI may be perceived as being impartial by a majority because it is a machine.

An underlying problem with generative AI such as ChatGPT is that even if we knew the complete details of the algorithm and the full list of texts used to train it, its biases and their origins would be largely opaque. This is because both the algorithm and the texts are too large for humans to read or comprehend. Much research would be needed to identify specific biases but this could only be done by analysing outputs.

Many of the weaknesses described here are also true of human beings as information sources: they are inaccurate, they hallucinate information frequently and they are rather biased (without realising it most of the time). AI has the potential to be actually less biased. This is particularly important because studies of information seeking tend to find that people rely on other people for information. AI has the technical potential to be less biased because it can be designed in this way. Unfortunately, the business models driving AI development often make the removal of some types of bias unlikely.

Fortunately, there are some emerging methods that can address these information issues. Retrieval augmented generation (RAG) promises to offer the eloquence of generative AI but more closely tied to a particular collection of reliable information sources. In a particular context reliable sources can be carefully chosen to train the model. Answers can also be tied back to original sources. This addresses some of the information issues that general services like ChatGPT suffer from.

Yet if we remain pessimistic about generative AI and its wider impacts, we can see it as threatening to degrade our whole information culture. There are several ways this could happen. The most obvious is that generative AI can be used maliciously for disinformation (Fergusson et al., 2023). The generation of propaganda and “fake news” is made easier with generative AI. It is increasingly difficult to distinguish a well- designed fake from authentic images or text. We tend to rely on factors such as the technical qualities of the material to determine the reliability of a piece of information we encounter. Unfortunately, much AI generated disinformation and misinformation can pass these tests. While the falsification of information for propaganda is an age-old problem, it is now easier and happening on a larger network scale (Aïmeur et al., 2023). AI has appeared at a time when there is a strong sense of a crisis around information. There is a potential for AI to degrade social trust in information as a whole and increase “plausible deniability”, reducing our ability to hold people to account. The impact is all the more insidious as there are indications that there are inequalities in society in the ability to correctly identify disinformation and misinformation (Bitton et al., 2024).

Generative AI could also have some damaging effects on the richness of the information environment within which we live (Capraro et al., 2024; Labusz & Nehring, 2024). The sheer amount of information of somewhat dubious accuracy and bland quality we encounter is likely to grow, as people use garrulous generative AI simply to create content at scale. A recent headline referred to us “drowning in slop” (Read, 2024). We are already flooded with information every day, a phenomenon known in Information Science as information overload. It leads to the necessity to skimming of content and so fragmented attention. It is easy to see how generative AI can further contribute to the deluge of information because it can create content at great speed. Much of this content contains misinformation, false information shared without intention to mislead, but still damaging . Whilst, AI is also helping us combat this overload through filtering, it may also be at the root of the growth of the problem in the first place. AI may be better at creating misinformation than detecting it (Aïmeur et al., 2023).

The risk to our information culture is not simply about the amount of content. There is a lack of diversity and richness built into generative AI content. Generative AI chatbots were trained on the mass of available digital content on the internet. Much of this is not of high quality and certainly systematically over-represents certain voices and under- represents others. The types of knowledge already under-represented digitally will be further marginalised. To a large extent generative AI has been trained on open content, including the web. But we know the web greatly over-represents the English language. One estimate is that 50% of web content is in English (Statista, 2024). The accelerating investment in English language content on the internet threatens to swamp minority languages. Lacking the same depth of training data the quality of generative AI for minority languages is a concern. So while AI’s ability to translate languages gives us greater access to knowledge, there are limits on how it copes with non-English material.

Nor are all voices in society represented fully. The types of values and assumptions built into generative AI by its designers centres a kind of mainstream Americanised culture, so that, as Rettberg (2022) puts it, generative AI may be multilingual but it may remain largely monocultural.

Generative AI is also designed to be uncontroversial and inoffensive; to be rather bland. The commercial drivers behind AI mean that it seeks to create a satisfied consumer not a questioning citizen. It wants to please us; it is not going to risk challenging our assumptions. The type of interaction it is premised on is therefore not the most enriching or empowering.

Equity of access and technology dependency

Even if we accept that generative AI is often helpful in creating knowledge, we need to address the issue of equity of access. Driven by commercial motives, access to the most advanced forms of generative AI requires a subscription. Its use also assumes technology and bandwidth, as well as digital skills and confidence. This means that generative AI may increase digital inequalities and as a result reinforce underlying social inequalities. There are many contexts where this is very important. One is education, where an attempt is always made to offer all students equal access to helpful technologies. Generative AI can be seen as offering an unfair advantage to students who can afford the latest services.

Meanwhile, we could argue that the various ways generative AI might assist our understanding also creates risky technology dependencies. This echoes past debates such as about how allowing calculators in the classroom threatens fundamental maths skills. In retrospect, these fears may appear to have been overblown. But it feels like there is a difference with generative AI. Actually, knowing arithmetic tables is useful, but is not a very advanced maths skill. A calculator just relieves us of the chore of arithmetic, leaving us time to learn other skills, including more advanced maths. In contrast, there is a difference with AI summarisation or translation tools if they degrade our own skills in understanding and prevent us learning. An AI generated summary cannot give us the same understanding of an idea in the same way as the hard task of reading the text ourselves to produce the summary. Similarly spell checkers and grammar checkers may simply reduce errors in writing given that remembering grammar rules and spellings is no longer important knowledge. But generative AI suggesting structures for our text and rewriting the tone of text might undermine our ability to write in our own voice. The proliferation of tools to help us understand and express our knowledge seems to threaten our development of necessary skills for knowledge expression. They can create a sort of passivity, as AI becomes more agentic.

It may be that as we become ever more used to receiving text generated with the help of AI that our sense that most communications are sincere expressions of human meaning may be degraded. AI is often anthropomorphised: described as human-like. The very term Artificial Intelligence implies that the technology does more than mimic some aspects of human understanding. The conversational interfaces of current AI reinforce this idea too. For example, in their statements AI often refer to themselves as if they were people not automated systems. This misrepresentation is an aspect of the opaque nature of AI. Little effort is being made to fully explain how AI really works. The lack of transparency of AI is an informational issue. The technologies underlying AI are genuinely hard for the lay person to understand. AI’s ability to learn may make it hard even for designers to make the outputs of AI transparent. Modern generative AI is largely opaque in terms of tracking the path from its inputs to its outputs because both the algorithms and training data are so large. Having said that we use technologies we do not fully understand all the time. We may not know how a car works but we can still understand how to drive it safely.

Often we hear the story that AI will automate routine work and leave us more time to do interesting or “creative” tasks which supposedly only humans can currently do. But it is also possible that AI will perform complex tasks such as creating a presentation from a report or summarising some long texts, leaving us with just routine tasks such as checking accuracy. Unfortunately, both stories have equal credibility. A lot turns on how the drivers for productivity in any particular context play out. Simkute et al. (2024) explore the potentially interruptive and harmful impacts of help if it is misdesigned. AI is potentially interruptive, disturbing the flow of our thought rather than enhancing it.

Fundamentally, if we shift in our role from knowledge creators to AI output checkers, we can lose sight of the overall task. This could make some work much less satisfying.

Reflecting on these many information problems, it is essential that using AI safely requires a critical and questioning approach. These AI skills or literacies are discussed further in Chapter 6. However, this construes the issue as largely an individual one. From our socio-technical perspective we also want to emphasise the importance of wider societal and global power structures as shaping the nature of AI. The societal governance of AI is as important as our individual skills in using it.

The environmental impact of AI

It is easy to think of the digital as somehow without a real-world impact on resources because it does not have an obvious material aspect. In fact, the digital has a significant environmental footprint (Smith & Adams, 2024). One estimate is that 40–50% of companies’ carbon emissions are attributable to digital technologies. The environmental impact of computing such as AI occurs at several points through the supply chain (Estampa, 2024; UN Environmenal Programme, 2024). The manufacture of computing devices requires mining for metals and minerals, such as copper, gold, lithium and cobalt. Some of these mining operations are very exploitative in character and the raw materials are used wastefully. Recycling of materials in technology equipment remains poor. IT generates further carbon emissions from transporting devices around the world. There is also the electricity required to manufacture and to use computers to perform AI operations. This growing demand seems to be being met currently by less clean energy sources in the form of fossil fuels. This growing demand is also causing construction of new data centres, again with huge environmental impacts. Another form of environmental impact of the digital is from the use of water to cool data centres.

Furthermore, these “infrastructural harms” fall disproportionately on the majority world (Valdivia, 2024). For example, extractivist mining worst effects Global South countries, including Congo. Much e-waste is dumped in Africa.

Generative AI has a particularly strong environmental impact. Training and using generative AI is demanding in terms of energy consumption. Hence, during 2024, Google and Microsoft failed their carbon emission targets because of AI development. The high performance GPUs often used to run AI processes are particularly environmentally impactful. These thoughts cast doubt on the sustainability of Big Tech’s ability to deliver the benefits of AI to all.

Of course, there is also the potential that AI is used to assist with the fight against climate change, such as by improving crop yields or monitoring climate change impacts. It can help by enabling people to work from home, predicting traffic flows to make transport more efficient, reducing waste, predicting energy needs to make power generation more efficient, and helping consumers manage their use of energy more efficiently (Royal Society, 2020). We also must think about a fair benchmark for impact. Tomlinson et al. (2024) suggest that the environmental impact of a human writing a section of text or producing an illustration is actually greater than performing it quickly through AI, when we take into account daily environmental impact of the human author and power demands of the computing device they would use to perform the task.

However, these calculations do not consider the whole hardware lifecycle impacts of AI.

The issue of environmental impact is exacerbated by the failure of Big Tech to be open with data about the impact of their AI. Their energy consumption data is not published to know exactly the balance of impact or make informed choices about whether and how to use AI. If it were, we could be hopeful that the motivation to develop technologies with least environmental impact would be stronger.

The character of the AI industry

Underlying many of the above concerns about AI is the perspective mentioned in Chapter 1 about the potentially exploitative and extractive nature of much of the AI industry. If we are hoping for inclusive, equitable and sustainable benefits, it is questionable to place these expectations in the hands of large corporations driven by profits. Yet it is they that have pioneered the development of search platforms, recommendation systems and generative AI. The most popular generative AI such as ChatGPT, Copilot and Gemini have these links. Ultimately their uses of AI are geared towards generating profit and creating happy consumers not critical citizens.

From the recent history of such organisations, we have seen how they have extracted user data as a valuable commodity, largely in defiance of legal protections such as the EU’s data protection law, general data protection regulation (GDPR). Current generative AI seem to perform badly also against GDPR requirements. However, the risk from generative AI seems more around the extraction of information content. To train generative AI, organisations such as OpenAI have treated the web and much copyrighted material as available to harvest for their commercial benefit (Tyagi, 2024). There is a strong sense that this may be in breach of intellectual property rights. Just as they have treated our data as a free resource to extract, now it is our knowledge.

The Big Tech companies are not particularly inclusive employers. The US IT industry is young, white and male. This matters because those who produce IT systems tend to reproduce their own assumptions within them. There is strong evidence of a link between the narrow makeup of the IT industry and the biases in how AI technologies like AI are designed (Perez, 2019). For example, there are recurrent patterns in search results, translation tools and image generation of assumptions about the gender of different professions showing gender and racial bias (Nicoletti & Bass, 2023). This reflects a broader failure of a non-inclusive workforce to include or engage wider communities in the development of AI.

Underlying this pattern of inequality are the strong cultural associations between technologies and white masculinity. This is not to say there is some sort of inherent link between gender or race and AI or any technology, but to point to the historical control of technology by men and the cultural associations that make it harder for women to participate in the industry (Cave & Dihal, 2020).

We also know that creating generative AI was dependent on exploiting precarious labour to perform essential tasks such as content moderation and labelling, mostly in the majority world. In order to train generative AI there is actually a lot of low level routine processing work to tag and label content. This was often outsourced to precarious, low-paid labour in countries such as Kenya. Some of the content they were charged with filtering was rather traumatic in character.

The magical seeming abilities of generative AI often rest on these less than glamorous, out of sight infrastructures. If we are to be optimistic about AI’s impact on knowledge access and creation, we have to believe in some sort of alignment between the interests of these massive powerful corporations, public interest and social good.

Technocolonialism

The benefits of AI are far from equally available globally. Indeed, the way the AI industry works can be seen to reflect a pattern of coloniality, in which monopolistic tech companies develop deeply exploitative relations in regions such as Africa in a pattern echoing colonial rule (Nkoudou, 2023).

Digital infrastructure in Africa is largely owned by Western companies. China too has ambitions to use technology to promote its influence. The Big Tech companies have achieved widespread connectivity, but it has also given them a control over the infrastructure that enables them to extract data, exploiting the relative lack of legal protections in the region.

There is limited software development capacity within Africa, which inhibits the creation of AI rooted in local values and suited for context. AI developed to solve problems in western contexts is often promoted as if it is useful in every context, but is unlikely to work so well elsewhere. Birhane (2020) gives the example that the use of AI in early breast cancer detection through analysing mammograms was promising in the Global North. It is far less effective in Africa, probably because of the age structure of the population and limited support. Here, different approaches developed locally are needed. Because context is important, Africa needs its own healthy AI development capabilities. Currently, where Africans do have employment within the AI lifecycle it appears to be mostly in routine even traumatic tasks such as data labelling and filtering.

While a miner in Brazil earns 5 EUR per hour, a data labeller earns less than 2 EUR per hour and an AI engineer at OpenAI makes 75 EUR per hour.

Valdivia, 2024

The way that AI is trained on predominantly Western content but promoted as universally applicable perpetuates epistemic colonialism, the privileging of certain ways of knowing (such as science) and the erasure of others, including indigenous knowing. For example, the precious linguistic diversity of Africa is under-represented in the training of AI and potentially undermined by the further of powerful tools optimised for English.

As the previous section showed, the damaging environmental impacts of hardware lifecycle mining and e-waste are felt disproportionately in the majority world, including Africa.

At the policy level the African voice is largely unheard in the international debate about defining responsible AI. We want to be hopeful that African based solutions can be developed locally and based on local values and needs. Nkoudou’s (2023) ten recommendations for a decolonised appropriation of AI in Africa give a path to make this possible. In essence these express the need to strengthen local development capacity to address local issues in context sensitive ways based on local data. This goes far beyond the governance of just AI.

Current trends

AI seems to change rapidly. Are there more reasons for optimism or pessimism?

Several current trends are very positive. One is RAG which ties generative AI to a particular body of carefully chosen material, increasing information quality and the ability to reliably point to sources for AI’s statements. Another is smaller language models that require less content training data than LLMs but achieve the same level of performance. This means the threshold for creating generative AI systems is lower, so undercutting the monopolisation of generative AI by Big Tech which is at the heart of so many of the problems we have discussed. Many more responsible actors can create generative AI systems with a more ethical emphasis. The reduced amount of training required for a small language model also lowers its environmental impact.

On the other side of the coin, many are predicting the increasing impact of AI agents. AI agents are intended to perform a specific, complex task with some degree of autonomy. Agentic AI is goal driven rather than content creation driven. But it could take on informational tasks such as resolving consumer questions and complaints drawing on multiple sources. This direction of travel would surely fill us with more concern. There have to be grave doubts about AI’s ability to grasp contextual aspects of decision-making or to understand human responses and emotions. There is the risk that agents aid the deflection of responsibility for wrong decisions. They imply more loss of human agency as AI takes on complex tasks. The ambition to create agents seems to reflect the discourses around AI that emphasise a “productivity” agenda and threaten job displacement.

Conclusion

Examining the capabilities of AI, especially generative AI, we have a sense of an exciting possibilities for our access to knowledge and expression. Adaptivity, conversational searching, summarisation and translation seem to offer hopes of greatly enhanced knowledge experiences. Yet a closer examination reveals a host of problems: the privacy and surveillance risks of recommendation and filtering; the inaccuracy and bias in generative AI outputs; the potential degradation of our information culture; impacts on equity and technology dependence. the violation of intellectual property rights; the character of the AI industry and its impact on environmental sustainability.

We have usefully scoped the question has revealed its complexity. We must reflect on whether we think the balance of benefit and disadvantage. To understand the implications for different knowledge using and creating contexts, we need to think through differing effects in different domains of activity such as work, learning, consumption, social media consumption and use, for the citizen and voter. In each of these contexts, the balance might play out a little differently.

It is important also to recall Information Science’s stress on equity, inclusion and sustainability. For the young, employed, city dweller in global north there are many immediate benefits from the capabilities of AI. We also need weigh up the experience of minoritised groups: such as the experience of older people or disabled people. We also cannot focus exclusively on the privileged context of the Global North.

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Chapter 3AI and research

DOI: 10.1201/9781003545163-3

Central to the question addressed in this book about the potential for AI to transform access to and creation of knowledge is its impact on knowledge in the sciences and other scholarly disciplines. This reflects Information Science’s interest in scholarship as lying at the heart of societal knowledge, with the important proviso that there are many other forms of knowledge also to be recognised, some that have historically been wrongly ignored or even erased.

The title of a recent Royal Society report conveys an important message about the impact of AI in scientific, technical and medical (STM) subjects:

Science in the age of AI: How artificial intelligence is changing the nature and method of scientific research.

Royal Society, 2024

AI is being used to find patterns in big data, from telescopes in astronomy, physics experiments, satellite data and the proliferating data collecting sensors and devices. AI is also in use in large scale models and simulations like digital twins, which are virtual models of real-world systems. Activity is intense in fields as diverse as medicine, materials science, robotics, genetics and computer science. To take a couple of examples, AI is having a big impact on materials discovery through modelling the properties of a potential new materials at a scale and speed not practical with manual techniques. AI is also very important in climate change monitoring, helping to build forecasting models for complex environmental systems.

Another impact of AI could be in task automation, including labour intensive activities such as laboratory experiment workflows. The long-term direction of travel for science could be “The AI Scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its findings by writing a full scientific paper, and then runs a simulated review process for evaluation” (Lu et al., 2024).

The push towards science based on big data implies more interdisciplinary collaborations to combine analysis and interpretation capabilities. Challenges remain around infrastructure, access to data, heterogeneity of data, management of sensitive data and reproducibility.

Perhaps it is obvious that STM is being transformed by AI. But we are also seeing changes in social sciences and humanities. As information scientists, the authors are interested in developments across disciplines. But in their research practices the authors are more familiar with social science and humanities work, so for this book it has been decided to pick out a few in depth some examples of how AI may transform these areas of scholarship by not just making them more efficient but asking new types of question.

Sentiment analysis

AI can open up new types of research that were impossible or impractical before. Sentiment analysis, is an example of this. It involves using AI to process large volumes of text to help answer new types of research questions related to public sentiment or opinions. Before AI, expensive, slow and labour-intensive surveys might be needed to elicit public opinion about a topic, limiting the number of issues that could be investigated and the frequency and nature of investigations about them. The combination of widespread posting to social media sites and the development of sentiment analysis AI applications has made it easier and faster to access greater volumes of data about many topics. For example, public opinion might be elicited about political issues from Twitter or X, about popular and unpopular aspects of different types of hotels or about reactions in comments to online influencers (Wankhade et al., 2022). Even retrospective opinions can be sought by analysing historical social media posts. Sentiment analysis AI can detect sentiment in texts millions of times quicker than a human, making large scale analyses possible.

Before generative AI, sentiment analysis was implemented through a variety of linguistic techniques that leveraged the fact that certain words are typically the bearers of sentiment, so detecting and interpreting terms like “good”, “terrible” and “recommended” could be translated into estimates about the presence, direction and strength of sentiment in a text. Whilst early software often used lists of sentiment terms and their typical strengths (Taboada et al., 2011), later software took advantage of generative AI to also detect the context of each word in its sentence to help detect sentiment more accurately (e.g. Hoang et al., 2019). With modern generative AI it is possible to get even more accurate sentiment estimates by taking into account more background knowledge and a greater ability to interpret the entirety of a text, rather than just the words near sentiment terms (e.g. Lossio-Ventura et al., 2024), and in fact sentiment term lists are no longer needed for generative AI because it “understands” the concept of sentiment and this makes them easier to apply to multiple languages.

It seems likely, that the increased accuracy of generative AI based sentiment analysis will increase the range of potential applications of sentiment analysis. A drawback of using only moderately accurate sentiment analysis is that any patterns found (e.g. males like product X more than females) could be due to biases in the algorithm. The influence of such biases seems likely to decrease as the overall accuracy increases.

Research evaluation

For over half a century, the field of scientometrics or bibliometrics has attempted to develop and assess quantitative indicators to support research evaluation, mostly using citation counts as the raw data. For example, the Journal Impact Factor (JIF), is a widely used and controversial indicator for the impact of an academic journal based (loosely) on the average rate at which the articles in the journal get cited (Seglen, 1998). At the article level, there are also various indicators for scholarly impact, such as the raw citation count and variants that are normalised for the field and year of the article (Waltman & van Eck, 2013) so that they can be more easily compared. The recommended role for these indicators in research evaluation is to support but not replace expert judgement (Wilsdon et al., 2015). This is because research quality is often through to comprise three dimensions, originality, rigour and impact (societal and scholarly) (Langfeldt et al., 2020), with citations only directly, but still partially, reflecting half of one of these (scholarly impact).

Generative AI has the potential to challenge citation analysis for research evaluation purposes because it has been shown to address some of the weaknesses of citation data, although it may introduce new limitations. This is possible because there is now a body of evidence showing that in various contexts, ChatGPT (usually 4o or 4o mini) has some ability to estimate the quality of journal articles (Saad et al., 2024; Thelwall, 2024a,b). In particular, if fed with a set of journal articles from a field and asked to score them for quality, ChatGPT’s scores are likely to correlate positively with expert scores in almost all fields (Thelwall & Yaghi, 2024). Surprisingly, this capability works with article abstracts alone and full texts are not needed. In fact, ChatGPT may make worse estimates if fed with full texts (Thelwall, 2023ab). Thus, ChatGPT has an ability to estimate the quality of academic research in almost all fields by interpreting the authors’ claims in their abstract.

For articles that are at least two years old, ChatGPT score estimates probably correlate more strongly with expert quality judgements in most academic fields (Thelwall & Yaghi, 2024). Since the power of an indicator is perhaps best measured by its correlation with human judgement, this makes a strong case for the value of ChatGPT estimates. In addition, they have the substantial advantage that they can be used for new articles (e.g. less than three years old) that have not had sufficient time to accrue enough citations for robust citation-based indicators. Thus, they seem to be more powerful and more widely useful than citation-based indicators.

ChatGPT quality predictions also have important disadvantages. At the time of writing, the reason that ChatGPT quality estimates tended to correlate positively with human estimates is not known, although it seems likely that it centres on interpreting authorial claims in abstracts. This uncertainty and LLM models being too large and complex for human understanding means that ChatGPT quality estimates are currently opaque and are never likely to be fully transparent. In contrast, citation data can be relatively transparent in the sense that each citation can be tracked down to its source document. Transparency is important in research assessment (Wilsdon et al., 2015), so that those evaluated can challenge mistakes or misleading results, so its absence is a substantial limitation for ChatGPT. The lack of transparency is perhaps ameliorated by ChatGPT’s ability to provide an explanation for its quality scores, however.

A second limitation is the potential that the results could be manipulated accidentally or deliberately. Deliberate manipulation could occur if authors start to tailor their abstracts for high ChatGPT scores rather than to maximise informativeness for human readers. This would be counterproductive from a scholarly communication perspective, undermine the value of ChatGPT predictions, and be a waste of time.

A third limitation of ChatGPT quality score estimates is that its biases are unknown. Thus, it might be achieving its positive correlations with human scores by leveraging the prestige of the authors or publishing journals. This seems unlikely since most tests so far have not reported journal or author data to ChatGPT, but it is possible since it might be able to connect articles to authors or journals from its existing knowledge.

Because of the above limitations, evaluators must think carefully before using generative AI to replace citation indicators in research evaluations. An example of good practice is the use of generative AI to estimate the quality of grant applications, with the results used to create a list of potentially lower quality submissions to consider rejecting without a full review (Carbonell Cortés, 2024). This is good practice because the approach was extensively tested before being applied, the grant applicants were told in advance of the procedure and given a chance to opt out, and human experts made the final judgement by checking the ChatGPT recommendations carefully.

Synthetic datasets

A new and perhaps strange application of generative AI is the development of synthetic patient datasets to overcome the ethical overheads of much important health-related research. The issue here is that some health-related research needs patient medical histories or other medical information to analyse. For example, a researcher developing software to detect lung cancer from patient scans would need a sample of scans with and without cancer to work from and test the accuracy of the software. This means that the patients involved must be asked for consent and the people in charge of managing patient scans must have an ethical approval process to tell them when they can share their images (Gelling, 2016). The permission process might take a year and consume the time of those involved in the application process, slowing the research, or might fail if patient permissions could not be obtained.

A generative AI solution to this problem is to create synthetic health data from real health data in a way that the synthetic data is authentic enough for research purposes despite not being associated with real patients that require permissions (Calvo-Lorenzo & Uriarte-Llano, 2024; Qian et al., 2024). Synthetic datasets might be shared with lower or no ethical overheads, speeding research and avoiding the time spent on ethical considerations. Of course, creating realistic synthetic data is a technical challenge but it is one that AI makes possible and, at least in theory, any size dataset could be generated with no extra effort.

Multimodal digital oral history

If it may seem obvious that science will be changed by AI, there is also a long history of digital humanities where computational techniques are applied to research in the humanities. The digitization of heritage material has allowed scholars to use AI techniques to analyse material at scale. For example, they have been enabled to search for changing patterns in ideas and the use of language across large corpora of historical material. Rather than relying solely on the close reading of texts, they can analyse material through “distant reading”. Developments in AI may deepen this strand of research, enabling humanities scholars to ask new questions. To illustrate these possibilities, this section describes suggestions about the way that AI could transform oral history research, as suggested by Smyth et al. (2024). Oral history is the recording, analysis and preservation of people’s verbal accounts of past times. Oral histories provide a rich resource for the interpretation of historical events enabling us to hear the voices of people talking about life and events in the past. It reflects our interest in everyday lives and the perspectives of “ordinary” people, as opposed to the experiences of the rich and powerful. A particularly important focus of oral history is the documentation of experiences of traumatic historical events. There are also strands of research interest in how oral history testimonies, rather than being simply true, reveal insights into the working of human memory and how the dialogue with interviewer and the context of interview shapes what is said.

Hearing the voices of people talking about the past is a very powerful experience. However, oral histories are typically accessed in research through transcripts of the original sound or video recordings. It is a cause of regret among scholars that transcripts rather than recordings are typically used, because this involves a significant loss of meaning and context that are gained from “close listening” to the original sound of a person’s voice. It is simply that it is more practical technically to access transcripts than navigate large multimedia corpora.

There are rich channels of communication between humans through bodily positions, gestures, facial expressions, tone or energy in the voice and even silences. These are not captured in transcription of recordings into text (at least in usual transcription conventions). For example, Persanha and Sala (2021) focus on breathing as accessible biological signals and suggest systematic connections between detectable breathing patterns and emotions. The sensory aspects of the oral history interview are essential to conveying its full meaning, especially its emotional dimensions. The reliance on textual transcription is so significant because to a large extent by using text only taking out the emotional dimension and reinforce over rationalistic interpretations of human behaviour.

AI technologies have the ability to support researchers to analyse for such data and at scale. They could enable the researcher to identify breathing patterns or qualities of silence across interviews. AI could pick up patterns of or gesture and body position and help the researcher navigate this aspect of the data in oral history interviews. This could open up new types of question about the history of emotions, such as which aspects of traumatic events trigger the strongest emotional reactions.

Of course, there remain significant barriers to such multimodal digital oral history. One issue is bias in the training of AI. By definition from oral history we are most interested hearing lesser heard voices, but algorithms are usually trained on commonly studied languages. With less studied languages, dialects and accents where there is less training data, error rates are higher. Indeed, it has been found that speech recognition of Arabic often performs badly and can even add references to war and violence that are not in the original words. This is because the AI was trained on news reports reflecting the slanted way that the Arabic world is represented in the media. There are indeed profound biases in the technologies because of the availability of training data (Persanha & Sala, 2021).

Studies have shown patterns of bias against gender and race, and “negative biases against middle and working-class socioeconomic status, male children, senior citizens, plain physical appearance and intellectual phenomena such as Islamic religious faith, non-religiosity and conservative political orientation” (Rozado, 2020). At a more practical level, many oral histories were sound only recordings not videos, so there is a lack of important visual context. If a video of an oral history interview is based on an online call then it is likely that only the upper body of the interviewee is visible. More reliance for analysis would then need to be placed on facial and hand gestures rather than full body positioning. Many recordings will not have been recorded in studio conditions so key sound data needed for advanced AI analysis may not have been captured. Furthermore, ethically such processing of oral history data may go beyond the spirit of what was agreed by the interviewees. Nevertheless, the example of multimodal digital oral history gives us some flavour of how AI might usher in a new paradigm of humanities research in one area.

Conclusion

This chapter has explored the potential of AI to transform scholarship. It is not simply that AI may enable research to be faster or more efficient, though this could be important. AI may also allow new types of research questions to be asked. By selecting just a few examples across social science and humanities we have hopefully pointed to the great possibilities promised by AI. Of course, there remain problems around access to the technologies, researcher skills and so forth. These will be explored further in the next chapter.

References

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Chapter 4Generative AI and research practice

DOI: 10.1201/9781003545163-4

In the previous chapter, we saw how AI is having a deep, long-term impact on science. On a more immediate level, generative AI has potential uses across many forms of research. Indeed, since the launch of ChatGPT, there has been a flowering of new research-oriented technologies leveraging the power of generative AI in ways customised to the needs of researchers (Baytas & Ruediger, 2024). Many are being offered by new entrants to the research technology marketplace (e.g. Scite, Scispace, ResearchRabbit, Consensus), some by Big Tech companies (e.g. Google’s notebookLM) and others from more traditional players in the sector like publishers (Elsevier’s Scopus, Clarivate’s Web of Science) or research software companies (e.g. Nvivo). This is not yet a mature technology and many of the new applications exist only in beta or have limited capabilities. They are evolving rapidly at the time of writing. It is highly likely that after this first flowering there will be a rapid phase of consolidation of services, with only the most popular or best financed surviving. However, a review of what is currently available offers a strong indication of the likely direction of travel of AI for research, at least from the point of view of the technology. Reflections on the development towards what is technologically possible will also need to be balanced by a consideration how commercial drivers might shape the marketplace for such tools, and how the culture of disciplines will shape the capability and willingness of researchers to take up the technologies.

To summarise the potential uses, we can consider AI applications to four of the main stages of the research lifecycle: the literature review, data collection and management, data analysis, and writing up. AI could also have uses in the routine administration of research projects such as in time management and resource allocation, and in facilitating communication, but we will focus here on more research specific uses.

Reviewing the literature

A key focus of the current crop of AI applications for research is for interrogating the published literature. Research is always based on first seeking to understand what is known about a question by drawing on what has already been published. Academic literature is inherently hard to digest because it is in-depth, specialist and often expressed in technical language. But it is packed with information such as findings from data analysis, including charts and diagrams, method considerations, literature reviews, and data in the form of cited references. The published literature for most subjects is also large and rapidly expanding, making it hard for researchers to keep up to date, even in their area of specialism. There is therefore huge potential for AI to assist researchers in assimilating the literature, both at the ideation stage of research and then throughout a project.

Emerging tools leverage AI’s ability to summarise whole texts, or extract elements, such as finding a definition or method in a paper or a table comparing papers against some criteria. This accelerates the researcher’s ability to navigate and understand relevant literature.

A particularly novel feature of generative AI is that it enables a kind of conversation with selected literature. The researcher can ask questions of a text or group of texts, such as about common methods or findings. Responses can map closely to where in the original texts a point is made to allow the researcher to check the reliability of the generative AI answer. One service, Consensus, also seeks to offer a summary of how the literature stands in relation to a particular point of debate. While it is important to use the right prompts to have these conversations, applications help by suggesting questions and offering to explain further. It is easy to imagine other add on features such as highlighting less trustworthy material like preprints (papers still awaiting peer review). As the literature is constantly updating, personalised digests can support keeping up to date. Typically, current applications work with quite a small number of texts, but could be able to scale up to whole bodies of literature.

Other tools seek to leverage references by mapping out authorship, giving an overview of a body of literature and visualising authors in a field to see who is most influential or to determine the most common research methods in use.

Translation and integration of non-English material may be another way AI will impact literature reviewing.

One ultimate direction of travel is towards what has been called a Living Systematic Literature Review, a comprehensive and systematic analysis of literature that is constantly kept up to date (Elliott et al., 2017). AI support could be at different stages of a living review, from designing and carrying out searches, screening papers for relevance and quality, summarising results and drawing out implications and recommendations for practice (van Dijk et al. 2023). We are currently a significant way off being able to do all of this without any human expert intervention. But certain parts of the process seem to be particularly suitable for the application of AI, such as screening publications for relevance. As techniques improve the potential to navigate large bodies of literature at scale could have a fundamental impact on the quality of research by greatly strengthening how the literature is used.

Data gathering and processing

AI can assist in the process of gathering and processing data for research. It could be used for various data cleaning and “wrangling” tasks such as simple tidying of data or validating that they meet quality criteria. AI may also support more complex tasks such as seeking to create structured data in tables from unstructured data (like a free flowing text) or integrating data that has been collected to different standards. The ability of technology to tirelessly and consistently process material at scale makes this potentially invaluable. AI also supports writing computer programming code. So if a researcher needs to develop code to process their data, generative AI may well support this effectively.

One very powerful application to research data gathering is the accurate transcription of interviews and focus groups. Transcribing an audio or video recording of an interview has always been a time consuming and tiresome process. It is expensive to outsource. Perhaps very good typists can gain familiarity with the data through doing transcription, but generally it is a time-consuming chore. While there are many weaknesses with current automated transcription services, it is easy to imagine that as they are trained on more and more voices they will become accurate, even with strong accents or less frequently studied languages. This will save researchers’ time, but also allow better integration of the processes of data collection and analysis. The gap between doing interviews and analysing them will be reduced, allowing more effective linking of data collection and analysis.

Another data management task might be in generating documentation and metadata to describe data for data sharing. AI might also be used to take on challenging tasks such as anonymisation or redaction of sensitive data. For example, in social media research it has become good practice to transliterate actual quotes to something with the same meaning to avoid deanonymisation of speakers in public spaces. This would be a task where generative AI might assist in suggesting the best transliterations.

AI also has the capability to support gathering data in the questionnaire survey process. For example, it could offer an interactive element to an online survey, providing a responsive introduction to the research, assisting respondents in understanding specific questions, or gathering extra data. By this logic one could imagine increasingly automating interviews too. But for some topics it is possible that interviewees would be more forthcoming with an AI interviewer, because the interaction feels more anonymous, and because it could operate fluently in multiple languages. It seems likely that over time this will emerge as a valid practice in certain contexts.

Another interesting speculation is that AI may be so good at producing text that it can be used for simulation of mass opinion currently collected by survey. The previous chapter mentioned an application of this on a small scale in ethically challenging health research. Grossman et al. (2023: 1108) suggest that generative AI “may supplant human participants for data collection” because they can accurately present what a wider population might say in an interview or survey, with far lower effort and cost. They could at least be helpful in predicting likely responses to be tested with real populations.

Data analysis

Core to empirical research is data analysis. One of the promises of tools like generative AI is to undertake initial data analyses of spreadsheets of quantitative data or a corpus of qualitative data, like interview transcripts.

Given its facility with language, generative AI appears to be potentially helpful in processing a body of text like interview transcripts to suggest codes or even themes. With carefully worded and iterative prompting generative AI may have the ability to generate useful descriptive coding. Existing tools already offer summaries of interviews, parts of interviews or other data, suggest codes, with an explanation, and propose definitions of codes. They allow researcher to ask questions of their data in a dialogic way. They can operate across languages, producing summaries in one language of a text in another. They are unlikely to replace human data analysis, but will offer a second opinion or even come to be seen as a way to validate an analysis.

Another interesting application would be to ask generative AI to produce an analysis of data from a particular mid-level theory perspective or from a particular meta-theoretical perspective (e.g. a feminist perspective). It feels improbable that it would be able to do this with any depth or nuance, but a model trained for this purpose might be effective enough to be useful.

AI can also help perform statistical analysis of quantitative data. Ultimately, the researcher’s knowledge of the subject, the data, and understanding of the meaning of statistical tests should remain the arbiter. It is also useful for business contexts where the depth of specialist analysis is less critical.

Academic writing and dissemination

Another part of the research process where it has been suggested that generative AI will have a big impact is on the publication and dissemination of research results. The primary way research is published is through peer reviewed journal articles and books. Generative AI’s ability to help plan the structure of writing, to suggest improved phrasing and style, and to proofread text for grammar and spelling all seem to promise to make good academic writing easier. It may be beneficial to use generative AI specifically designed for academic writing styles than a general model like ChatGPT, however, because there are distinct differences.

In this role generative AI is particularly welcome for researchers who want to publish in the dominant international language for scholarship, English, but for whom it is not their native language. This would be a significant benefit, as the advantage that the dominance of English in academic publishing gives native speakers is unfair.

Perhaps even more exciting than improving academic writing, AI has potential to support the creation of diverse outputs for disseminating research, such as in lay summaries of complex research for the public. These could be as simplified texts or diagrams or even podcasts. At the time of writing, notebookLM is surprising researchers with an impressive ability to summarise the findings of complex research in the form of a naturalistic conversation in a podcast style. The communication of science results to wider publics may be fundamentally assisted by generative AI’s ability to rewrite texts for different audiences. This is very important as it has long been hoped to engage wider publics with science. Generative AI may offer ways to do this.

Thus, AI has the potential to enhance if not transform many different research processes. This might be, as now, in the form of several specialist personal productivity tools. Or it could operate more at the level of a research platform that integrates multiple functions for researchers. One reason ChatGPT is so popular is because it performs a wide range of tasks. It is also possible that some of the capabilities of AI may be provided to researchers as a whole via publisher platforms, such as real time lay summaries for the public or policy makers, rather than it continuing to be an individual researcher task.

Risks and barriers

Set against these potential uses of AI within research, there are many risks and barriers (Kim, 2024). Some of these doubts reflect the issues discussed in Chapter 2. We have already considered issues around accuracy and bias in generative AI. A level of tolerance of inaccuracy is appropriate in certain contexts but scholarly enquiry is not one of these. Researchers would need to be very careful about accuracy when using AI to support writing, but particularly in any sort of role in analysis, be that of the literature or data. It seems that AI could help with the categorisation of data or literature with descriptive codes and themes. But because it does not understand human contexts or causal relationships it is likely to work at a relatively superficial level. It would be likely to perform badly at the more nuanced aspects of coding for latent or implicit meaning or indeed for what is not said. It is likely to be better at applying an existing coding scheme deductively, rather than originating a new scheme. If it offers suggestions that the researcher can approve or not, it needs to leave the researcher in charge.

Rigorous checking of the outputs of all AI uses would be essential. Finding consistent ways to declare how AI was used would also be needed. Ultimately the human researcher needs to be seen as entirely responsible for every element of a piece of research (Directorate-General for research and innovation, 2024). Yet, ensuring accuracy is not made easier by the opacity and lack of transparency of current AI, that means the nature of accuracy and bias is not known. It is hard to account for problems if the nature of the problem is not clear.

There are also significant implications for the reproducibility of research. There is increasing emphasis in many disciplines on ensuring this by describing methods in great detail and sharing data to ensure that other researchers can repeat a study to check the results. The black box nature of AI would make replication harder to achieve. However much a researcher documented their process, it would be impossible to exactly reproduce what they had done to check it because generative AI evolves over time and contains random factors at its core. Ultimately a human researcher has to be deemed responsible for the final output of research. Worryingly, generative AI also seems to make the fabrication of data and research results easier for dishonest researchers. As fabrication is already a problem in science because of the intense pressure to publish, this is a real concern.

There are also great potential issues around privacy and ownership. Given the centrality of confidentiality to research with human subjects, researchers should be very reluctant to place data with AI unless there were absolute assurances that this data was not stored or used to train a model. Such use would also need explicit consent from interview participants.

Current tools to support literature reviewing often rely on open access material, but this still has its limits. Also, they tend not to be very transparent about how particular papers are selected and prioritised. They allow users to upload content, such as the papers the researcher has downloaded through subscription sites. However, it is a grey legal area in many legal jurisdictions whether this is a breach of copyright. In some it may be that analysing texts may be allowed under a legal exception for Text and Data Mining.

Perhaps, piecemeal uploading of a few items might be legitimate, particularly if the service provider is not storing/using this data for training its algorithm. But bulk upload appears to be very risky. Publishers will encourage researchers to use tools built into their platforms. This is legally safer, but assumes that the publisher platforms offer such tools. Furthermore, because publishers only have access to that part of the literature they published (and open access content) they are not capable of working across the entire literature. For the purposes of a systematic review this is inadequate.

Fundamentally, access to tools and platforms is likely to be expensive and so only available to a few privileged researchers.

Reaping the potential benefits of AI is dependent on the capabilities of research systems to enable researchers to take the opportunity of these benefits, ensure they are offered equitably and sustainably, and manage the risks. There are many factors that might shape this capability. Funding is one. There is currently huge investment by research funders in AI but this is not equitably distributed. Technology infrastructure is another key requirement. There is also a need for clear policies, establishing what is appropriate use. But in this period of change when the exact character of AI capabilities is in flux it is hard to offer clear guidance. Publishers are seeking to define how AI can be used, with an emphasis on disclosure of use and limiting its use to the improvement of expression of results. However, there is a grey line between rewording and suggesting further implications of findings.

Other factors for AI take up include the skills and attitudes of researchers. There are gaps in many researchers’ digital skills, let alone understanding and receptivity to using AI. It may take a long time for methods to be accepted where the majority of senior researchers learned their craft in the pre-digital or pre-AI era. While narrow uses to support routine aspects of research such as transcription might be quickly adopted, aspects that are seen as core to the nature of research such as analysis, are like to face much more resistance since they touch on the core skill of research and even the identity of the researcher. Significant reluctance is surely justified. For example, the idea of simulating human responses using generative AI proposed by Grossman et al. (2023) would be highly contentious to scholars especially because there is typically a focus on uncovering experiences of minoritised populations in an attempt to address social inequalities. The essential human connections forged through data collection, especially in co-produced research, could be inhibited if AI is introduced into this process. And as a highly skilled activity, in data analysis it seems unlikely that AI will supersede the human researcher.

In Chapter 2, we reflected on the way that commercial drivers for AI shape how users are constructed as subjects. Developing a similar argument in the research context, Paulus and Marone (2024) explore the way that the marketing of qualitative data analysis tools reshapes the discourse around research. Much of the marketing material for AI research tools implies that understanding of data can be automated as if the meaning is inherent in the data already rather than something built up through effortful human understanding. The task of analysing data is construed as tedious in contrast to the speed and interactivity AI offers. The novelty of AI is also used as an argument for using it, setting aside the question of the quality of the analysis is offers. Here the commercial motive to sell a product effectively reshapes the discourse around research, portraying analysis as boring and slow and AI driven research better merely because it is faster and supposedly exciting because it is based on technology. As technology gains a stronger foothold in scholarship, it is possible that such influences will reshape how it is talked about and done.

There is also a question about how equitably the benefits of AI for access and creation of knowledge in research will be spread. There is already an underlying problem of equity in science. Internationally, scholars in majority world countries have significant disadvantages in undertaking research, such as less access to basic research infrastructure of power, computing resources, up-to-date software, as well as limited funding and less well-developed research cultures, including policies and support to skill development. While generative AI would seem to offer a small rebalancing in terms of reducing stress on English language skills, if there are advantages to using generative AI they are much more likely to be available to scholars in the Global North simply because access is costly and the wider infrastructure to support research tends to be better. It is hard to see AI doing anything other than widen inequalities here.

Conclusion

This chapter has reflected on how generative AI is diffusing into the processes of scholarship. Some changes appear to be time-saving by undertaking routine tasks such as transcription. Because they are never overloaded or fatigued, AI tools have the potential to be speedy, consistent and tireless aides. There is a fear that this could increase the pressure on researchers to be productive (Chubb et al., 2022; Watermeyer, 2023). Other changes seem to be usefully lowering the threshold of skills required for certain tasks, such as to write code for research or reduce barriers to publishing in English for authors whose native language is not English.

The more interesting cases are where AI appears to make new things possible such as: engaging more deeply with the literature, engaging with literatures in other languages, making surveys more interactive, and accelerating qualitative data analysis bringing it closer to the ideal of simultaneous data collection and theorisation. Certainly at the time of writing generative AI has reenergised the research software market.

However, where the core skilled activities of research, such as analysis are being substituted, care is needed to ensure that the core value of research is not undermined. Ultimately, it is not yet if that these changes will fundamentally reshape science. There are likely to be very large variations across disciplines because academic tribes are rooted in very different values and practices.

References

Baytas, C., & Ruediger, D. (2024). Generative AI in education: The product landscape, Ithaka S+R, https://sr.ithaka.org/publications/generative-ai-in-higher-education/.

Chubb, J., Cowling, P., & Reed, D. (2022). Speeding up to keep up: Exploring the use of AI in the research process. AI & Society, 37(4), 1439–1457.

Directorate-General for Research and Innovation. (2024). Living guidelines on the responsible use of generative AI in research. European Commission, https://research-and-innovation.ec.europa.eu/document/2b6cf7e5-36ac-41cb-aab5-0d32050143dc_en.

Elliott, J. H., Synnot, A., Turner, T., Simmonds, M., Akl, E. A., McDonald, S., ... & Pearson, L. (2017). Living systematic review: 1. Introduction—the why, what, when, and how. Journal of Clinical Epidemiology, 91, 23–30.

Grossmann, I., Feinberg, M., Parker, D. C., Christakis, N. A., Tetlock, P. E., & Cunningham, W. A. (2023). AI and the transformation of social science research. Science, 380(6650), 1108–1109.

Kim, S. J. (2024). Research ethics and issues regarding the use of ChatGPT-like artificial intelligence platforms by authors and reviewers: A narrative review. Science Editing, 11(2), 96–106.

Paulus, T. M., & Marone, V. (2024). “In minutes instead of weeks”: Discursive constructions of generative AI and qualitative data analysis. Qualitative Inquiry, 10778004241250065.

van Dijk, S. H., Brusse-Keizer, M. G., Bucsán, C. C., van der Palen, J., Doggen, C. J., & Lenferink, A. (2023). Artificial intelligence in systematic reviews: Promising when appropriately used. BMJ Open, 13(7), e072254.

Watermeyer, R., Phipps, L., Lanclos, D., & Knight, C. (2023). Generative AI and the automating of academia. Postdigital Science and Education, 6(2), 446–466.

Chapter 5AI and scholarly communicationPeer review

DOI: 10.1201/9781003545163-5

Information Science is interested in whole process of scholarly communication. This relates to the processes by which scholarly knowledge is circulated. We have already discussed many aspects of this in the previous chapter, such as searching the literature and writing for publication. Another key aspect of scholarly communication is peer review.

Academic peer review is a process of using people with academic knowledge, typically subject experts that either have a PhD or are PhD students, to assess academic outputs for quality control, assessment, or improvement purposes. (It is usually seen as central to ensuring quality in research publication.) For example, journal article peer review typically involves an editor requesting two or three subject or methods expert academics to report on an article that somebody else has submitted. These reviewers should then make suggestions for improvement and a publishing recommendation (e.g. reject, accept, accept subject to stated revisions, reconsider after revisions). After this, the editor will make a combined publishing decision to the author, accompanied by the reviewers’ points. In contrast, post-publishing expert review involves usually senior academics scoring sets of published outputs for quality to inform a decision about funding (project or block grants) or employment (e.g. appointment, promotion and tenure).

The selection of reviewers for outputs is often an important and difficult task. If an article is submitted to a journal, then the editor will need to know what skills are needed to assess it and who in the world possesses those skills. If some potential reviewers decline, they will also need to identify more reviewers than needed. Selecting reviewers is therefore a knowledge-based experienced researcher task.

Peer review is time consuming because all academic outputs are unique and complex. Moreover, pre-publication peer review needs to be careful due to its quality control function and post-publication quality evaluations typically involve many articles. It is an expert task because of the knowledge needed to assess relevant aspects of the output. These combine to make it an expensive task in terms of the financial side, or the opportunity cost of the other work not done by the reviewers.

Although academics learn ideas about quality in their field, peer and expert review in more formal and systematic contexts often have explicit guidelines for at least some aspects of the task. Although there are many different factors that are considered important in some contexts, there are three near-universally acknowledged core dimensions: rigour, originality and significance (academic and societal) (Langfeldt et al., 2020). Thus, reviewers typically need to apply their expertise to judge the rigour, originality and significance of academic outputs.

Common uses of automation and AI to support peer review

Reviewer selection is commonly AI-assisted. For example, it is relatively straightforward to match an article’s keywords to the keywords entered by potential reviewers into a journal management system and this basic idea can be used to dramatically narrow down the range of potential choices. In practice, this is an AI task to do well because potential reviewers might not have keywords in the system and their expertise might need to be inferred from their previous publications. Moreover, the keywords for an article might not be useful in capturing the expertise needed for reviewing. In addition, the matching or semi-matching reviewers will need to be ranked in order of likely relevance for a reviewing task. Such systems can be highly useful to editors as reminders of already known potential reviewers as well as suggestions for new ones. Of course, the editor’s experience might be critical in ensuring that the selected reviewers are sufficiently able to cover all critical aspects of a paper.

Generative AI that has ingested academic areas of the web might aid reviewer finding by suggesting academics that are not already in a journal management system.

Nevertheless, suggesting reviewers is not a natural task for them because, in addition to General Data Protection Regulation (GDPR) issues about personal information, they store patterns rather than precise information and so would need to be paired with other systems to give precise answers. This might be addressed by a retrieval augmented generation (RAG) approach (Chen et al., 2024) that would allow generative AI to read in relevant extra information, such as text from a bibliometric database of PDFs, when making reviewer recommendations.

Plagiarism, including dual submission, is also now detected routinely by journal management systems. This can be done in a non-AI way, by simple text matching, but cleverer approaches are needed to make it practical to identify coinciding sections of text from massive existing collections – checking directly for all possible matches would be too time consuming.

A much rarer approach to support peer review is to automatically detect statistical errors in submissions. Some statistical errors can be detected automatically if a program can extract statistical test information from a paper and then check that the numbers match (e.g. Nuijten & Polanin, 2020). For example, if a t value is reported then the program could check that the associated p value is correct. It could also check in some cases that the reported statistics (e.g. mean, standard deviation) are possible for the type of data and sample sizes (e.g. the average of two whole numbers could never be 1.3).

Finally, AI can help with detecting image manipulation fraud, such as by identifying parts of an image that are identical to parts of other, unrelated images (Al-Janabi et al., 2023). This is important in some fields where images are a critical component of larger investigations and researchers seem tempted to fabricate them rather than conducting the extra work to produce them.

Potentials of generative AI for peer review

Generative AI has already been shown to be good at some aspects of peer reviewing. A simple way to use generative AI would be to feed in a submission with a request to review it or point out flaws in it (Zhou et al., 2024). AI has been shown to make many of the same recommendations as reviewers in some contexts (computing-related conferences). Because of this they could be used to suggest points to reviewers before they review, to help or speed the review. However, this would probably not be a good idea because it might bias reviewers or encourage them to read the submission less carefully. Providing suggestions after completing an initial review draft might be more helpful since these suggestions might point to issues that the reviewer has overlooked. Of course, this is also not a perfect solution because a busy reviewer knowing that the system might catch things that they overlook might be less careful in their original review. Other approaches might work, however, such as rewarding reviewers for a good review if they make many of the same points as generative AI (Lu et al., 2024).

A second type of peer review task that generative AI might help with is summarising reviews to support an editorial decision. AI seems to be good at summarisation so this is a natural task (Chauhan & Currie, 2024) although it is not clear that it would be helpful since editors might want to evaluate the quality of each review separately to help their decision-making.

One peer review aspect that generative AI can play a minor role in is decision-making. In addition to identifying problems with academic outputs they can predict review outcomes and assign scores to them. Although their results are not reliable overall, this capability is being used for triage purposes for grant reviewing. For this task, generative AI scores all applications and then the lowest scoring are sent to a few experts to check, with those judged by all reviewers to be too weak to be funded then rejected without a full review (Carbonell Cortés, 2024). This saves reviewer time by reducing the number of reviews needed overall.

Potential of generative AI for expert review

Generative AI has, surprisingly, been shown to have a capability to rate the quality of academic research, even based on titles and abstracts alone. More specifically, ChatGPT can be fed with the title and abstract of a published academic paper and then assign a quality score to it accompanied by a written justification. These scores correlate positively with human scores for the same articles in all broad academic fields (Thelwall et al., 2024; Thelwall & Yaghi, 2024). This is surprising because the score does not depend on an examination of the full text and seems to primarily leverage the author’s claims in their abstract. The correlations with human scores seem to be highest in the health and physical sciences and lowest in the arts and humanities.

Bibliometric information like article or journal citation rates is already often used to support expert review but is limited to the health and medical sciences and to articles that are two to three years old. Generative AI therefore has the potential to improve on this by covering almost all fields as well as new articles, substantially widening the scope for supporting human judgments. Any such attempt to do this should proceed with extreme caution, however, for several reasons. First, experts may have more experience of the limitations of bibliometrics than AI for research quality evaluations, so may place undue reliance on AI predictions. Second, authors might try to cheat the system by writing unduly positive abstracts. This could be misleading for human readers, or at least sub- optimal given that humans should be the intended audience for abstracts. Third, unscrupulous authors might try to game generative AI somehow. Finally, the biases of LLMs are unknown, so it is not clear whether introducing generative AI would be disadvantageous to some fields or topics.

Conclusion

As this chapter has also shown, AI is already very useful to support reviewer selection for peer review as well as plagiarism detection. It has already demonstrated an ability to check for a narrow range of specific statistical errors and omissions too. LLMs can further enhance AI support for peer review by providing suggestions for reviewers, synthesising reviews or making recommendations. For expert review, LLMs also have a reasonable ability to assign a quality score to academic journal articles, probably by interpreting authorial claims in abstracts. Nevertheless, there are substantial potential problems with copyright, law and ethics that should be seriously analysed before important new applications are considered. Nevertheless, enhanced AI support for peer review is now at least a technical possibility with LLMs at a scale not possible before.

References

Al-Janabi, O. M., Alyasiri, O. M., & Jebur, E. A. (2023). GPT-4 versus Bard and Bing: LLMs for fake image detection. In 2023 3rd International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA) (pp. 249–254).

Carbonell Cortés, C. (2024). AI-assisted pre-screening of biomedical research proposals: Ethical considerations and the pilot case of “la Caixa” Foundation. www.youtube.com/watch?v=O2DcXzEtCmg.

Chauhan, C., & Currie, G. (2024). The impact of generative artificial intelligence on the external review of scientific manuscripts and editorial peer review processes. American Journal of Pathology. https://doi.org/10.1016/j.ajpath.2024.08.002.

Chen, J., Lin, H., Han, X., & Sun, L. (2024). Benchmarking large language models in retrieval-augmented generation. Proceedings of the AAAI Conference on Artificial Intelligence, 38(16), 17754–17762.

Langfeldt, L., Nedeva, M., Sörlin, S., & Thomas, D. A. (2020). Co-existing notions of research quality: A framework to study context-specific understandings of good research. Minerva, 58(1), 115–137.

Lu, Y., Xu, S., Zhang, Y., Kong, Y., & Schoenebeck, G. (2024). Eliciting informative text evaluations with large language models. arXiv preprint arXiv:2405.15077.

Nuijten, M. B., & Polanin, J. R. (2020). “Statcheck”: Automatically detect statistical reporting inconsistencies to increase reproducibility of meta-analyses. Research Synthesis Methods, 11(5), 574–579.

Thelwall, M., Jiang, X., & Bath, P. A. (2024). Evaluating the quality of published medical research with ChatGPT. https://arxiv.org/abs/2411.01952.

Thelwall, M., & Yaghi, A. (2024). In which fields can ChatGPT detect journal article quality? An evaluation of REF2021 results. https://arxiv.org/abs/2409.16695.

Zhou, R., Chen, L., & Yu, K. (2024). Is LLM a reliable reviewer? A comprehensive evaluation of LLM on automatic paper reviewing tasks. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 9340–9351).

Chapter 6Libraries and archives

DOI: 10.1201/9781003545163-6

In this chapter, we consider the impact of AI on libraries and archives as key social institutions that promote access to knowledge and freedom of expression for all. Libraries and archives exist at many levels. National libraries and archives have the responsibility to collect a nation’s heritage in published and manuscript form. Given this remit they are accumulating vast collections, much of which have been digitised or were born in a digital form, and so amenable to the use of AI. Public libraries and archives house material for all members of the public and relevant to the heritage of a local area. They tend to be increasingly closely connected into local communities, especially underserved and marginalised communities, promoting skills and connectivity. Educational libraries, such as in schools, colleges and universities support learning and research. Some corporations also have library or archive services to support their decision making and promote their brand heritage. In an ideal world, libraries are a trusted public infrastructure for access to knowledge in each sector they operate in. They are also places where training programmes are held to learn knowledge skills. As such it is important to ask how AI might impact our access to the knowledge they contain.

The role of libraries and archives

Part of the value of libraries is that they are organised by information professionals with strong commitments to Information Science values, notably access to knowledge and freedom of expression for all. They also have a strong service commitment, with a focus on understanding and meeting the needs of their users. Librarianship is a collaborative profession which shares knowledge through its professional institutions, so that best practices in service delivery can be quickly emulated by comparable institutions. These attributes make the profession an important actor to ensure the benefits of AI are shared equitably and inclusively.

However, the role of libraries and archives should not be idealised and it must be recognised that there are some significant limitations on their role. Not every country has a well-resourced library and archive service. Not every local community has a public library, for example. In many contexts, reliance on libraries has declined due to book purchasing and the availability of a large amount of information online. It must also be acknowledged that historically libraries have not always been as inclusive of all forms of knowledge as they should have been. The historic privileging of published and written knowledge at the heart of the very notion of a library, has deliberately sidelined other forms of knowledge such as indigenous knowledge which is shared through spoken words and in everyday practices. Indeed, in the past, libraries and archives were partly a vehicle used by states to tell a particular version of history in which many narratives were marginalised or even actively erased. This means they were not as broad in what they collected as they should have been and how material was acquired and described reflected regressive assumptions. As a consequence, some communities do not feel a sense of connection to them as institutions. Indeed, in some cases there is a sense of alienation from library and archive institutions, because of their connections to historic and ongoing epistemic violence.

Historic schemes of classifying library material have reflected regressive viewpoints. It is not surprising that out of date assumptions and attitudes were applied to collections in the past. This reflects false beliefs in society as a whole. Libraries and archives are now committed to expanding the scope of their collections and decolonising their practices. This implies a respectful process of collaboration with local communities to ensure that the right material is collected, described and shared in appropriate ways, based on communities’ control over their own representation. But it takes time for people to recognise these changes and realise that libraries and archives are institutions that they can use.

Despite these significant limitations it remains important to the question posed in this book how libraries are impacted by AI because for many accessing knowledge a library remains a critical location for access to knowledge. This applies to researchers in fields such as the humanities but also local communities using public libraries. AI has the potential to improve access to collections of materials in libraries in a number of significant ways.

AI uses in libraries and archives

Digitisation of content that was originally in manuscript, printed text, images or recorded sounds remains a key task in expanding access to our heritage of knowledge. There is an argument to say that Optical Character Recognition, the technique of turning any of these types of material into machine readable, digital data, is AI itself. It is part of the “perception” dimension of AI. Certainly, AI is being used to improve the quality of digitisation, such as by being trained to be much better at understanding different handwriting styles in manuscripts or different voices. AI is also being used to improve the quality of digital objects in collections, identifying duplicates, poor quality scans or missing descriptive data. AI is excellent at these kind of tasks at scale.

Generative AI is being developed to provide descriptions of new items being added to collections. Libraries enable users to find information by attaching structured descriptions of objects, metadata, to material. It is currently an intensive process to manually describe an object, such as determine who is the author or what the subject of material is. AI can help here by picking up the more obvious elements of a description. It is unlikely that it can provide all the detailed structured metadata to describe a new item, without any human oversight, but it can speed up the process.

Even more exciting is the idea of using AI to categorise large digital collections. For example, it would be hard to manually catalogue a large collection of videos so people could search to find something that appeared deep in the content of a video. However, an AI produced transcript of each video could be provided to facilitate a detailed search across all its content.

This could be extended from text to images or sound. AI can be trained to identify types of objects pictured in a digital image. It can do this at a scale which would not be very practical (or an interesting task) for humans to do. This promises to greatly enhance our access to historical and born digital collections, by deepening collection description.

A further extension of this would be to train a model to analyse a large corpus of published texts, like scholarly publications. This is referred to as Text and Data Mining. The amount of scholarship being published every year has accelerated so much that conducting a manual literature review in some fields is becoming harder and harder. Using AI to discover patterns in a very large corpus of published material would make possible new discoveries and connections in already published work. Furthermore, this analysis could be extended to texts in other languages, to discover material in a very large collection of texts.

As mentioned in an earlier chapter, the epitome of this idea is the living systematic review. A systematic review is a type of analysis of published literature based on a very systematic procedure to ensure that the result of the search includes all relevant, high- quality material which is then analysed for reliable evidence of the effectiveness of particular interventions. This is then used as the foundation for evidence-based practice. A good example is the health field where a systematic search of all the literature about a condition is used to yield reliable guidance to health practitioners on treatments.

An exciting vision of what AI could offer is a living systematic review would be one that was automatically updated as new literature is published. Given the great speed of publication in fields such as health it is necessary. There remain significant barriers to achieving a fully automated process, such as the precision of the search, the ability of the computer to identify quality in research results or draw out conclusions for practice. But there is great potential in AI to accelerate the production of evidence-based advice.

Another important role that the collections in national libraries and archives, and some research libraries, can play is through their vast collections of data. These can be potentially used to improve generative AI. For example, libraries in non-English speaking countries hold unique corpuses of their language. This can play an important role in countering the dominance of English.

There is also the RAG technology that has already referred to in Chapter 2 and where libraries could play an important role. RAG promises to fix some of the informational issues with generative AI by linking the eloquence of a generative AI like ChatGPT with a body of trustworthy information that ensures the answers given are reliable. Libraries in every sector have collections of trustworthy information that could be used to train RAG.

Taking this to its epitome we could create what has been dubbed the Intelligent library (Cox et al., 2019). Rather than searching for a book to physically locate and read, our way of using a library collection could be much more like conversing with an entire collection of knowledge. Through the power of AI this would not have to be limited by format or language. This is an inspiring vision of how AI could fundamentally enhance our access to knowledge through libraries and archives.

As a multiple technology, AI offers many other benefits for improving libraries and archives, and so increasing access to knowledge (Hervieux & Wheatley , 2022; Cox & Mazumdar, 2022; Balnaves et al., 2024). AI-based chatbots can be developed to answer user questions and support them through information seeking processes. Predictive AI can be used to analyse user data to anticipate patterns in behaviour. For example, it could enable the library furniture to moved round to accommodate changing patterns of usage. AI has a lot of potential in managing the large amounts of data that libraries handle in the background, processing data such as loans of books.

These are real possibilities, but the current development of AI by Big Tech is disappointing because it is driven by commercial values and overrides many of the principles that animate Information Science. The commitments set out in Association of Research Libraries (2024) Guiding Principles for AI point to some of the ways that such enduring values are relevant to achieving more responsible AI.

Libraries democratise access to artificial intelligence tools and technology to foster digital literacy among all people. This principle underscores the library’s commitment to inclusivity, knowledge dissemination and the empowerment of individuals.

Libraries commit to understanding where distortions and biases are present in AI models and applications.

Libraries champion transparency and information integrity.

Libraries believe “no human, no AI.”

Libraries prioritise the security and privacy of users in the use of AI tools, technology and training data.

Libraries assert that copyright law in the United States and Canada is flexible and robust enough to respond to many copyright issues that arise from the intersection of technology and artificial intelligence.

Libraries negotiate to preserve the scholarly use of digital information. As champions of fair use and fair dealing, intellectual freedom and freedom of information, libraries assert that licenses should not restrict the scholarly use of digital information.

The potential of AI to enhance access to knowledge in library and archive collections is immense and multifarious. But the sector faces some significant challenges in achieving these benefits.

Challenges in the uptake of AI

Resource is a key factor. Libraries throughout the public sector are not very generously funded. Neither the path of buying in commercial AI systems or developing their own AI solutions using open source software are easily available. Commercial systems remain expensive. Not many libraries have technical development staff with the high level skills to develop AI solutions. Public sector pay levels mean that talent is drawn elsewhere. As a result, there are many projects that show how AI can be used, but turning them into full blown services is hard. Related to this there is a lack of case study evidence of what works that can help make decisions about how to prioritise resources. National libraries share a lot of ideas, but the efforts remain at the level of piecemeal experiment. It is important that governments invest in library services if the many benefits of AI will be equitably and inclusively, in a way commercially driven companies are unlikely to provide.

There are significant technical and legal barriers too. AI trained on modern material (e.g. contemporary images) may not be effective on historical data. Suppose you had an AI trained on images of contemporary house interiors. As these are widely available it is the most likely, and cost-effective data to train AI with. However, AI trained on such data would probably be quite ineffective in identifying features of historic interiors. Things simply looked different in the past. So the expensive business of training the models cannot rely on AI trained on contemporary data.

An important consideration for inclusivity here is that AI is much more likely to understand received pronunciation than strong dialect and accents. Yet the main driver for collecting tends to be to capture more marginalised stories, as we saw in the discussion of multimodal digital oral history in Chapter 3.

There are also legal barriers to Text and Data Mining. While UK law, for example, allows researchers to create a corpus of texts to analyse algorithmically, this is not the case in all jurisdictions. Commercial publishers may create TDM services but they often restrict their scope to material they own. This is profoundly limiting for the potential of the living systematic review.

A feature of AI is that it produces statistical predictions. Its results are always estimates with a degree of reliability. It can also be hard to fully explain its outputs, because the process of a machine learning from results is not based on a predetermined logic, but discovered in the data. This means that the descriptions it creates for historical material in library and archive collections may differ from handmade cataloguing. As a result it may be hard for to explain how results were produced, so that the user to really understand results they are presented with.

Even if the resource to overcome these challenges is not available in the short term, where libraries are likely to play a very important role is in promoting AI skills (Cox, 2023). Libraries have a strong commitment to training users to discover and use information critically; it goes hand in hand with providing reliable collections of knowledge. In parallel Information Science has had a central task of examining how people discover and use information. As we saw in Chapter 2, the informational challenges of generative AI are particularly significant. Unless AI applications are made vastly more transparent, it will be important that users gain an understanding of how to use AI effectively and insights into the informational problems of services like generative AI. Exactly what this literacy will look like is considered in the next chapter.

Conclusion

Libraries and archives are important access points for knowledge and so are a context where AI may offer significant impacts. Although historically they have not represented all forms of knowledge well, this is changing. AI can aid the description of materials at scale. Ultimately it could make how we access the richness and diversity of human knowledge much more direct and interactive. Libraries also play an important role in educating their users to navigate issues of information accuracy and bias. However, there are many barriers to the take up of AI, particularly resourcing.

References

Association of Research Libraries. (2024). Research libraries guiding principles for artificial intelligence, www.arl.org/resources/research-libraries-guiding-principles-for-artificial-intelligence/.

Balnaves, E., Bultrini, L., Cox, A., & Uzwyshyn, R. (2024). New horizons in artificial intelligence in libraries. Berlin, Boston: De Gruyter Saur. https://doi.org/10.1515/9783111336435.

Cox, A. (2023). How artificial intelligence might change academic library work: Applying the competencies literature and the theory of the professions. Journal of the Association for Information Science and Technology, 74(3), 367–380.

Cox, A. M., & Mazumdar, S. (2022). Defining artificial intelligence for librarians. Journal of Librarianship and Information Science, 56(2), 330–340.

Cox, A. M., Pinfield, S., & Rutter, S. (2019). The intelligent library: Thought leaders’ views on the likely impact of artificial intelligence on academic libraries. Library Hi Tech, 37(3), 418–435.

Hervieux, S., & Wheatley, A. (Eds.). (2022). The rise of AI: Implications and applications of artificial intelligence in academic libraries. Chicago, IL: Association of College and Research Libraries.

Chapter 7Ensuring the benefits of AI are shared fairly

DOI: 10.1201/9781003545163-7

In this chapter, we reflect on what can be done to ensure that societies gain the positive benefits of AI for equitable and sustainable improvements in knowledge access and creation.

There are many stakeholders involved in ensuring that the benefits happen, and we will focus on three: governments, developers and those who teach critical AI skills.

AI governance

Governments obviously have important roles in protecting our information culture and potentially regulating the AI industry to deal with some of the problems with current systems. In the first place, it would be excellent if governments made a strong general commitment to ensuring that an equitable and inclusive information environment is available. This should start from accepting critical nature of the rights to discover and express knowledge. One way to do this could be to invest more in our library and archival institutions, with their role in deepening access to knowledge and promoting AI literacies. Libraries offer a platform for raising awareness among all citizens.

There is much existing legislation and governance structures around areas such as privacy and IPR that may already protect us, to some degree, from AI. Strengthening legislation in certain areas, as the European Union (EU) is doing, could be highly beneficial. For example, under the EU AI act companies will be expected to declare how their models were trained. They could be prevented from simply appropriating knowledge content in disregard of intellectual property rights. They could also be required to introduce watermarking technologies that make it possible to automatically detect generative AI text. This would help in flagging AI-generated content to readers.

This would legally strengthen transparency and accountability. New laws about bad actors using generative AI to produce deepfakes may be needed. They could also have a requirement to publish full environmental impact assessments.

More fundamentally, state action could be used to change the nature of the AI industry, such as by promoting equity and inclusion in the workforce. This could be achieved through investments in education to diversify the IT workforce. The AI industry should be obliged to measure and control its various environmental impacts. Another concern is blocking the tactics that are often used by platforms to protect their monopolies. The monopolisation by certain platforms effectively renders our individual ethical choices empty.

Perhaps states are not very inclined to put these protections in place. The issues are probably not fully understood at the governmental level. Individual states may also lack the power to confront Big Tech. They are also using AI as a means of international competition. Given that the issues are global, it will take collective action by governments to work together to ensure that AI benefits society (UN, 2024).

Of course, the companies developing AI systems could commit to developing them in a far more responsible way. This would imply being transparent about how their systems work, such as about the training data used; putting in greater protections for users against misuses, for example, by watermarking outputs of generative AI so they can be automatically detected; involving stakeholders in the development of services; auditing and controlling environmental impacts. It seems that this may be possible while still attaining commercial profitability.

AI skills

Unless AI systems become much more transparent because of government pressure or developer action, it will remain necessary for the individual to have critical AI skills (Cox, 2024). These skills will enable them to discover, analyse and create knowledge successfully, safely and sustainably in the context of AI.

Thinking back to the benefits and issues outlined in Chapter 2 is a good guide to the kind of awareness that users of AI need. We do not think that this is necessarily about understanding the underlying technologies in any great depth. The combination of statistical models and computational techniques that make up AI is hard for the layman to understand, even at theoretical level. Many articulations of AI skills focus too much on defining technical terms like machine learning. Such definitions do not mean very much in our encounters with knowledge. A far more useful contextual awareness is of the kind of social processes involved in generating knowledge and disseminating it. For example, how science works or how media priorities shape news.

Given the current proliferation of AI-based tools, choosing the right one for the task is important. Different versions of ChatGPT have different capabilities and different web search engines use AI assistants in different ways. Even from a purely technical perspective, comparing these services is challenging, but we should also be weighing up the business model of the provider as this seems to be crucial to their ethical character, a point we will develop further. This is in itself a challenging task because as we have seen what is available is changing rather rapidly. In a context of rapid change, taking advice from organisations such as libraries can be very helpful.

A dimension of this that is not always mentioned is considering whether you yourself feel that the ethical issues with AI have been addressed. So, it is not just about what capabilities the AI offers, but also its wider social impacts. As we explored in Chapter 2, there are significant issues around the impact of business motives, fair employment practices and content use and environmental impacts currently practised by Big Tech in their development of generative AI. For example, we can make choices about which generative AI providers to use or which tasks to use them for based on their differential environmental impacts (Smith & Adams, 2024). Ethical issues cannot be an afterthought. Rather our very decision to use a particular generative AI platform must be informed by our understanding of how the organisation offering these services performs against such criteria. Currently many organisations are far from open about issues such as environmental impacts, but asking the question is important.

We should also be concerned about our safety and security. Most users probably realise that it is not safe to put personal information into free websites. We have learned a lot about personal data misuses in the last few years. Generative AI can also be seen as extending the extractive pattern of AI development. In order to train AI, our own content shared on the web has been mined, without permission. We should think about how our use of AI protects our own safety. It is now possible to opt out of the use of your usage data to train AI on many websites. At the moment, it may be too late to protect our intellectual property rights with our content having been fed into training the generative AI already. But placing a robots.txt on our website blocking further harvesting may be something we want to consider. We can also look at different web publishers’ stances on selling content.

Following from choosing the right generative AI service to use is understanding how to use it effectively. Taking generative AI in particular, there has been a lot of commentary on “prompt engineering”: how we ask AI questions. This could be about setting up systems assumptions, which sounds more like engineering, but if we just mean how to prompt AI it can involve simply wording our inputs to conversations with AI carefully.

Each service is different and changing, but there seem to be some general principles that are useful to observe:

Define the question you want the answer to as specifically as possible. For example, rather than ask something very general “how is privacy defined?” it would be better to ask “what is privacy from a virtue ethics perspective?” or “how is privacy defined in UK law?” or “What did Kant say about privacy?”

Define the context of the question you are posing. For example, you might be wanting an explanation of a concept for a novice or subject expert. For example, you could ask privacy to be defined for either a novice or a philosophy student.

Define the kind of output you want in your prompt. For example, you might want a short answer or a bulleted list or it to be expressed at a particular reading age.

Iterate the prompts. For example, ask the same question in different words a few times and reflect on the different answers gained. Synthesising the different responses will give you a more optimal output. You can also ask the generative AI what suitable prompts are. You may provide training material or examples to assist in an appropriate answer being given.

Have follow up questions to your original request. You could ask for more details, views from different perspectives or for a critical analysis, for example: “What is a feminist perspective on privacy?”

Ask for sources that support the response. These will need to be carefully checked, but could be helpful.

Ask for AI to state its confidence in its answers

Ensure that you understand the guardrails in place and decide whether to turn them off in your prompt. For example, surprisingly, it may be necessary to tell the generative AI to be honest and direct in its answers. If posed incorrectly questions can be wrong because the generative AI is politely not challenging your assumptions.

Prompt the generative AI to think about considerations around accessibility and inclusivity.

Being better at prompting also means making fewer requests and so having less immediate environmental impact.

It is important that everything produced by generative AI is checked for accuracy. As we saw in Chapter 2, AI is prone to errors and can also be biased in its responses. As well as inaccuracies there are certain patterns in how it creates information that are problematic. We know some of the characteristics of generative AI material that you may want to actively counteract:

It tends to overuse certain words, such as delve, explore, dynamic, embrace, captivate, resonate and so on.

It tends towards a rather bland enthusiastically positive tone.

It often does not reach a conclusion on one side or the other in relation to the question at hand.

It can reproduce social stereotypes or biased assumptions.

It tends not to cite sources adequately, if at all.

It can be seen as conservative in the sense of lacking creativity.

These points are clues to identifying AI produced text in the knowledge discovery process and also indications of how generated content should be adjusted during knowledge creation.

Actively thinking about how you plan to correct these issues has to become part of practices of generative AI use. Generative AI can be actively prompted itself to correct such systematic errors. But it is more likely to be something that we ourselves must do. It can be linked to reflecting on where in the process of information gathering and creation you feel it is appropriate to use generative AI. This might be at one or some of these different stages of knowledge creation:

Understanding difficult to understand concepts

Assisting in designing searches for information in search engines

Structuring thoughts before writing

Changing phrasing in tone or style

Correcting grammar and spelling mistakes

Ultimately the objective should be to understand generative AI as a useful assistant to discovering information sources, analysing ideas and data to determine a conclusion and creating writing in one’s own voice. Part of generative AI literacy is understanding what is appropriate use in a particular context. This could be determined by the rules set locally, be they explicit or implicit. In the context of writing something for a learning assignment or publication, there are likely to be guidelines set by relevant institutions. Even if there are not explicit rules, it is implicit that honest communication needs to be open about acknowledging how generative AI contributed to knowledge creation.

Reflection on the impacts of generative AI use is important. It is easy to see generative AI use as merely increasing efficiency, echoing the discourse that it is about productivity. But sometimes such efficiencies have unintended consequences: such as loss of skills or growing dependence on technology. When we use AI to summarise texts we are losing some of the learning we would have gained from reading the text carefully ourselves. Our skills in reading are likely to be negatively affected. Studies of the use of generative AI in learning contexts have pointed to negative effects on social engagement with other learners and on agency in learning. As with any technology there is a need to reflect on the impacts of its use. This mirrors a wider responsibility towards the information culture. We should be trying to only share accurate information.

Pragmatic understanding: The individual can use generative AI effectively and interpret the information it produces critically.

The individual can pick the right tool for the task, in the context of the proliferation of services (including alternative generative AI to ChatGPT).

The individual learns to use the chosen service effectively for a specific task.

The individual interprets generative AI outputs critically, given an understanding of how they work and their limits (includes identifying AI generated content when they encounter it).

Safety understanding: The individual can use generative AI safely.

Reflective understanding: The individual can assess and take action to manage the impacts of AI on their experience in the educational context.

Socio-ethical understanding: The individual understands the societal impacts of AI, including IPR issues, impacts on information culture, social impacts, environmental impacts and undue power of Big Tech.

Contextual understanding: The individual understands how to use generative AI appropriately in a particular context and make their own use explicit, as appropriate.

AI skills are important for any individual encountering AI generated material. But they are hard to develop and maintain. The speed of change in AI platforms is hard to keep up with, so how we use them needs also to be constantly updated. In this account it has been assumed that we are consciously using generative AI, when we go to a particular website. But this is not always the case. AI can be working in the background, so that it is impacting our access to knowledge and freedom of expression is half hidden. How it works is often a commercial secret, so there can be deep barriers to having completely sound AI skills.

Conclusion

Governments have an important role to play in setting up controls to make AI more understandable and safer. Companies developing AI also need to be more responsible in their practices. In the meantime, it is important to have critical AI skills. The chapter has sought to define generative AI skills, along five dimensions of understanding: pragmatic, safety, reflective, socio-ethical and contextual.

References

Cox, A. (2024). Algorithmic literacy, AI literacy and responsible generative AI literacy. Journal of Web Librarianship, 18(3), 93–110.

Smith, H., & Adams, C. (2024). Thinking about using AI? Here’s what you can and (probably) can’t change about its environmental impact. Green Web Foundation. https://www.thegreenwebfoundation.org/publications/report-ai-environmental-impact/.

UN. (2024). Governing AI for humanity, www.un.org/sites/un2.un.org/files/governing_ai_for_humanity_final_report_en.pdf.

Chapter 8Conclusion

DOI: 10.1201/9781003545163-8

This book has explored whether Artificial Intelligence (AI) is transforming knowledge access and creation in a significantly positive, equitable, inclusive and sustainable way. This question is one that is central to society in the era of AI. The approach taken in this book is rooted in the authors’ commitment to the values of Information Science, which places the right of all to find and create knowledge at the centre to its theories and practice. In most areas of life, those rights are important, be that as a consumer, employee or citizen. It is also essential to the progress of scholarly knowledge.

Chapter 2 examined the way that AI, including generative AI is changing knowledge access and creation in everyday contexts. The possibilities are exciting. A student writing a dissertation is used as an example. The benefits from filtering, recommendation, summarisation and translation and in terms of interactivity and adaptivity feel significant. However, close consideration of such experiences also raised many questions especially about information accuracy, bias and impacts on our information culture. We also considered the environmental impact of AI. Many of these issues are rooted in the nature of the AI industry, with its potential links to inequalities, including technocolonialism.

Chapters 3, 4 and 5 relate to how AI may change the nature of scientific knowledge creation, since this is a central area of interest for Information Science. Chapter 3 gave some examples of how AI is transforming the questions scholarship can ask across the sciences, social sciences and humanities. Chapter 4 examined the potential impact of generative AI on key scholarly processes: literature reviews, data gathering and analysis and academic writing. But it also explored some of the issues and barriers with this impact. Chapter 5 considered the role of AI in research quality, through its use in peer review.

Chapter 6 reflected on the impact of AI on libraries and archives as key social institutions facilitating access to knowledge.

Chapter 7 considered how governments might regulate AI to ensure the benefits of AI to knowledge are equitably and sustainably shared. It also considered what would constitute AI literacy for individuals to gain the most benefit from AI and minimise the risks.

The book has clarified the context of the original question it posed. Yet a definitive answer to that question cannot be presented for a number of reasons. The balance of benefit in everyday information use seems quite difficult to assess. We need to think carefully about particular contexts, such as that of being an employee, a consumer, a social media user/creator, a learner or a citizen, to determine how the benefits and risks play out. Also, it is a hugely complicated question when we try to avoid normalising the young, employed city dweller in the Global North, and consider the experiences of the elderly or those with a disability and those that make up the majority of the world who live in the Global South (recognising the great diversity of experience there too). This book is also written towards the beginning of the historical moment of generative AI, and therefore it has to be taken on trust that some of the predicted benefits will be realised. We have guessed at the direction of travel of the technologies, when it is important also to accept that actually it is quite unpredictable (often intentionally so in the interests of Big Tech).

Some of our encounters with AI, particularly generative AI, are explicit, we know where AI is happening, hence the proliferation of speculation about the need to develop skills to collaborate with AI. However, much AI is embedded in the infrastructure of information moderating, filtering and even creating the information we access. The problem here is that is that the operations of AI in these contexts is largely invisible and we may be unaware how it is shaping the information we see. And the drivers are not necessarily favourable to empowering us to create knowledge. Platforms have a vested interest in creating a good consumer who is happy with a service. They place strong limits on the extent of diversity in knowledge expression. They have little interest in creating a critical citizen. And it is not an easy choice to opt out of such monopolistic platforms. AI is taking part of our agency in thinking.

The truth is that we are all still searching for the right historical parallel to understand AI. Is AI like the calculator when it first came to the classroom, seemingly threatening core maths skills, but now taken for granted? Or is it like Wikipedia, treated with suspicion at first now largely accepted as a reliable source? Is AI as significant as the invention of the Internet or the printing press, as some have suggested? AI has even been compared to electricity. More ominously is AI like the power loom of the first industrial revolution and are we the handloom weavers?

At the time of writing most predictions about AI point to the increasing diffusion of AI agents that perform complex tasks independently. This reproduces the usual dilemma around AI that it may make our life easier and free us to do more interesting things; or that it may take agency away from us, and leave us as passive recipients of information. It is hard to be optimistic when the control of AI is in the hands of Big Tech companies. A performance discourse will drive corporate adoption of AI with significant job displacement. Governments will develop surveillance technologies. Social media companies will enliven their platforms with artificial characters barely distinguishable from human participants. This is unlikely to increase the amount of meaningful content we are encountering. The ultimate purpose is to create sticky websites.

It is also worth acknowledging the opportunity cost of investment in AI, when it may be a diversion from investing in other socially beneficial investments. How is the hype around AI distracting us from other pressing problems such as the climate crisis?

On the other hand, if AI is governed for social benefit there is more chance that AI will be beneficial to our access to knowledge. This will be particularly possible if societies invest in public knowledge creation systems in academia and in places like libraries and archives. This will increase the likelihood that the benefits are shared inclusively and equitably in society, and so more socially sustainably. RAG and small language models seem to offer hope here. Ensuring benefit will also be made possible when we are all better equipped with critical AI skills making it harder for irresponsible uses to survive.

A few more qualifications to how we have discussed AI must be acknowledged. This book has focussed on text creating not image creating AI. Of course, we live in a visual culture where information is often conveyed by visual means. So the impacts of AI on the creation and use of visual material (including moving images) needs further thought. The book has also said little about developments of AI in China. As a major force in the development of AI what happens there will be important in many contexts.

Finally, this book has focussed on one area of impact of AI. Beyond the core values of Information Science it must be acknowledged that there are limits of the importance of knowledge access and creation. Many in the world lack basic rights to food, shelter and safety. Of course, it is vital that these needs to be met. But knowledge access and creation is part of the solution for achieving that.

Index

Adoption barriers, 51–52, 67–69

Archives, 62–69

Artificial intelligence: agents, 29;

definition, 7–9;

environmental impact, 24–26;

governance, 71–72;

industry, 7, 26–27;

literacy, 72–78;

skills, 72–78

Bias, 19–20, 22, 40

Big Tech, 7, 26–27

Data gathering, 46–47

Data analysis, 48

Data cleaning, 46–47

Data sharing, 47

Digital divide, 4

Digital humanities, 38–40

Disinformation, 21

Ethics, 3–6, 17, 22–23, 50, 53

Generative AI: character of, 1–2;

knowledge discovery, 12;

issues: commercial drivers, 7, 26–27, 52;

environmental impact, 24–26;

hallucination, 20;

inaccuracy, 19, 50;

information culture effects, 21–22;

inequity of access, 22–23, 53;

opacity, 20;

privacy, 51;

technology dependence, 16, 23;

technocolonialism, 27–29;

prompt engineering, 48, 74–75

Global South, 27–29

Inequality, 4, 7, 22–23, 53

Information culture, 21–22

Information science, 3–6

Knowledge: creation, 14–16;

discovery, 11–12, 15

Large language models (LLMs), 9

Learning, 14–17

Libraries, 62–69

Literature reviewing, 45–46

Living systematic review, 46

Misinformation, 21

Oral history, 37–40

Peer review, 55–60

Recommendation, 11–12, 17–19

Reproducibility, 50

Research evaluation, 35–37

Responsible AI, 67

Retrieval augmented generation (RAG), 20, 29

Search, 12

Sentiment analysis, 34–35

Science, 33–34

Socio-technical perspective, 6–7

Sustainability, 5, 24–26

Synthetic data, 38, 47–48

Science, technical and medical (STM), 33–34

Technocolonialism, 27–29

Technology dependence, 23

Translation, 13, 46, 49

Understanding, 12–13

Writing, 48–49