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This bibliography aims to evaluate relevant resources found in an information search relating to the topic of the importance of Information Literacy in the era of Artificial Intelligence. The context is how libraries can adapt to the technology and equip the public with the necessary skills.

Focusing on a selection of 10 resources, including Journal articles, Blog posts, Online articles, courses and academic frameworks and noting how results may have been influenced by the rapid developments in the last five years.

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From the course: How to Research and Write Using Generative AI Tools

How to work with the responses from a generative AI chatbot

(keys typing) (bell dings) - Working with an AI shouldn't be about helping you do less work or helping you produce more work. It should be about helping you achieve better. And the way that we do that is by adding our own human abilities on top of the AI response. It's important that you don't make the mistake of thinking of AI in the same way as a search engine. With a search engine, you put in a query and it returns results. Boom, you're done. With AI, it's more of a journey. You need to refine your request to get closer to what you were after. It's a back and forth. It's a conversation. That's why ChatGPT starts with the word chat. You should think about the AI as a collaborator rather than as a servant. You need to give as well as take. What you put into the conversation defines what you get out of it. If you don't like what you get, give feedback so that the AI knows exactly how to improve its response. If it gives you something you like, say so and ask the AI to expand on that thinking. If you get an idea during the conversation, share it with the AI and ask it to explore that concept. The results you get are defined as much by your input as by the AI's output. Don't see the results it gives you as a finished product. The AI is not human, although it does a pretty good job at mimicking human output. Your humanity is important. You need to apply your own abilities to the output to make it even better, to take it from adequate to excellent. I also recommend that you double-check the accuracy of the output. You can get output that sounds entirely plausible but is in fact completely made up and you don't want to be working with inaccuracies and lies unless, of course, you work in politics. And finally, be honest if you're using AI. Don't try to pass off AI-generated work as your own. There is nothing to be ashamed of in using AI to produce better results. In fact, that makes you really smart because it gives you an advantage and benefits the organization you're working for. In the next lesson we're going to start playing with actual prompts. So get ChatGPT loaded up on a browser and let's get going. (keys typing)

Download courses and learn on the go

Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.

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How to work with the responses from a generative AI chatbot - How to Research and Write Using Generative AI Tools Video Tutorial | LinkedIn Learning, formerly Lynda.com

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After watching this video, you will be able to collaborate effectively with ChatGPT. AI tools are better at some jobs than humans but not as good at other jobs, so it's important to understand which tasks to do yourself and which ones to give to the AI.

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A New Future for Library Discovery Empowered by AI

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How Well Does ChatGPT Handle Reference Inquiries? An Analysis Based on Question Types and Question Complexities

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How Well Does ChatGPT Handle Reference Inquiries? An Analysis Based on Question Types and Question Complexities

Lai, Katie

College & Research Libraries, 2023, Vol 84, Issue 6

2150-6701

10.5860/crl.84.6.974

See if it's available through your library.

Find your institution

Not finding what you're looking for?

Your source for trusted research content

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How Well Does ChatGPT Handle Reference Inquiries? An Analysis Based on Question Types and Question Complexities

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How Well Does ChatGPT Handle Reference Inquiries? An Analysis Based on Question Types and Question Complexities Lai, Katie College & Research Libraries, 2023, Vol 84, Issue 6 2150-6701 10.5860/crl.84.6.974 See if it's available through your library. Find your institution Not finding what you're looking for?

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BIRSS, D., 2023. How to Research and Write Using Generative AI Tools. [online]. LinkedIn Learning. Available from: https://www.linkedin.com/learning/how-to-research-and-write-using-generative-ai-tools  [Date Accessed 6 December 2023].

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BREEDING, M., 2023. ‘A New Future for Library Discovery Empowered by AI’, Computers in Libraries, 43(7), pp. 20–21. Available at: https://search-ebscohost-com.ezproxy.rgu.ac.uk/login.aspx?direct=true&AuthType=ip,shib&db=lxh&AN=172032534&scope=site [Accessed Date 4 December 2023].

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Highly rated course instructor, David Birss is an author speaker, and broadcaster on topics such as innovation, creativity, and ideation. His course is available to users on LinkedIn Learning. Hosting a collection of short, concise videos that offer practical guidance on how to leverage ChatGPT for various aspects of research and writing. Following the methods taught in the course, learners can enhance their abilities to utilize AI prompts effectively. While the course doesn't explicitly mention information literacy, the principles and techniques align with those concepts. To summarise, the course's credibility as a source is relatively strong as LinkedIn is an established network. The information circulated on users' feeds may contain bias due to the algorithm of the usage of the app. However, LinkedIn Learning (formerly Lynda.com) would be required to source an experienced instructor and develop course content, making it more credible as a source of learning.

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The author of the article, Marshall Breeder is an independent consultant, writer, and library conference speaker and is the founder of Library Technology Guides. His article discusses the future of libraries and how AI may empower the role. This article utilizes advanced terminology specific to the field and as a result an enquirer without that background, may not find this source as accessible as other sources in the bibliography. However aside from that, the articles list subjects such as AI, information technology and library public services which are relevant to the search query and covers those subject well within its 1400 words. This article also differs as it delves into the details of how AI will impact the future of libraries, one of which is user expectations as well as discussing key differences between earlier web-based online catalogues and how much more user-friendly the search is by utilising keywords.

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Generative Artificial Intelligence: Trends and Prospects | IEEE Journals & Magazine | IEEE Xplore

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JOVANOVIĆ. M., et al., 2022. Generative Artificial Intelligence: Trends and Prospects, in Computer, vol. 55, no. 10, pp. 107-112 [Accessed Date 4 December 2023].

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This journal article investigates the trends and prospects of Generative artificial intelligence across multiple formats such as language, music, computers and gaming among others whilst evaluating how they can be integrated into a workflow for everyday life and creativity of the public. Rating the applications in terms of efficiency, fairness, ethics and accountability based on feedback loops displayed in table format.

This article is well illustrated and has been cited 17 times implying that it has had influence. The authors hail from academic backgrounds Jovanović is an assistant professor at Singidunum University and Mark Campbell is, the chief innovation officer for EVOTEK, California. Based on this this would be a credible source. Although the article isn't as text-heavy as other sources, it is well-referenced and the broad topics addressed in the article makes this a well-rounded source. Outlines how GAI is utilised in everyday scenarios.

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LAI. K., 2023. How Well Does ChatGPT Handle Reference Inquiries? An Analysis Based on Question Types and Question Complexities. [online] American Library Association. Available from: https://ezproxy.rgu.ac.uk/login?url=https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib&db=llf&AN=173421502&scope=site [Accessed Date 6 December].

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Practical, responsible and human-centered: the future of AI in libraries

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Last week on this blog, we told you about a new leadership brief from the Urban Libraries Council, which outlined ways public libraries can integrate artificial intelligence (AI) into their library services and functions. In that brief, the ULC noted that it "looks to position the library as a resource for the practical and responsible application of generative AI".

"Practical" and "responsible" are key words to note, especially the second one. Much of the current discourse about artificial intelligence revolves around the dangers of its irresponsible use. On this very blog we have looked at potential data-security risks posed by the use of ChatGPT in libraries, and explored at the ways that AI can contribute to the spread of misinformation online.

On the other hand, we have also outlined various ways that libraries can incorporate AI technologies to improve their services. These emerging technologies - artificial intelligence, machine learning and natural language processing - will only become more ubiquitous and woven into the fabric of our daily lives, and public library systems and university libraries alike must prepare for this new reality.

See also:

How do libraries use AI?

We have covered this topic at some length previously, including in our recent blog post about tech trends shaping the future of public libraries. In a library setting, AI can be useful in supporting academic research, in optimizing collection development and in facilitating data-driven decision-making, among other applications.

Here's a brief refresher on some other potential uses of artificial intelligence - particularly AI-powered automation - in the library environment:

Collection development

Libraries can automate the process of adding books, digital resources and other materials to the library catalog. This includes importing metadata, generating call numbers and updating records.

Hold and reservation management

The process of placing and managing holds on books and other materials can be automated. Patrons can reserve items online, and the system will notify them when the items are available.

Interlibrary loan requests

Libraries can use an automated system to request and receive materials from other libraries. This can streamline the process of borrowing items not available in the library's collection.

User account management

Patrons can manage their own library accounts online, including updating contact information, changing passwords and renewing materials.

Visitor data analysis and recommendations

Library automation can collect and analyze patron data to better understand usage patterns and preferences, which can inform collection development and program planning, optimizing resource management. Automated recommendation systems can also suggest relevant books and materials to patrons based on their preferences and borrowing history.

Librarians rise to the challenge

The fact is that most libraries have used AI applications for years. As the Association of College and Research Libraries (ACRL) observes in the introduction to its recent publication, The Rise of AI:

Librarians are uniquely positioned to rise to the challenge that AI presents to their field. Libraries and their like have existed for millennia; they progress with society, altering and adapting their services to meet the information needs of their communities. Academic libraries today have greatly expanded their digital offerings, not just to include electronic books or journal articles but also to support software application discovery and use. Some academic librarians might say they lack a foundational knowledge of AI or that they are ill-equipped to speak on the subject, and yet they have likely been interacting with AI through the different types of software applications they support. At the very least, they have encountered and mastered the art of the search algorithm.

AI enables libraries to lead

The ACRL is referring specifically to university libraries, but Urban Libraries Council President and CEO Brooks Rainwater says public libraries are also well-positioned to drive innovation.

"As generative AI tools become more accessible, effective and less expensive, there are new opportunities for libraries to lead," Rainwater says. "While there are persistent concerns that must be addressed on AI being used for misinformation, the opportunities in front of us are manifold. This includes responsible applications that improve efficiency, speed up communication and serve as a useful tool for showcasing and validating library services and resources."

Why is privacy a concern?

It's all about that big data.

Chris Chiancone, Chief Information for the City of Carrollton, Texas, noted in a recent LinkedIn blog post that in order to function effectively, AI systems often rely on huge quantities of data - data that can include sensitive information about users. This, Chiancone says, raises valid concerns about user privacy and data security, and libraries are not immune.

"Libraries have a responsibility to protect their users' information, and the use of AI technologies can complicate this task," he writes. "For example, if an AI system is used to recommend books to library users, it would require access to the users' reading habits and preferences, which could be considered sensitive information."

Regulating data and privacy protection

AI technologies are subject to existing data protection and privacy regulations, including the following:

General Data Protection Regulation (GDPR): A comprehensive regulation for organizations operating within the European Union (EU) handling the personal data of EU residents. It chiefly focuses on data protection, privacy and personal data rights.

California Consumer Privacy Act (CCPA): A data privacy regulation in California that grants specific rights to consumers regarding their personal information. It requires businesses to disclose their data collection and sharing practices, enabling consumers to opt out of sharing their personal information.

Other regional regulations: Other countries and regions have also implemented data protection and privacy laws for AI systems like ChatGPT. For instance, the Personal Data Protection Act (PDPA) in Singapore and the Lei Geral de Proteção de Dados (LGPD) in Brazil.

A comprehensive AI law

By the time you read these words EU member states, the European Parliament and the European Commission will likely have reached agreement on the final text for the AI Act, touted as the world's first comprehensive AI law.

As PressReader reported in a blog post on ChatGPT and data security this past June, the proposed legislation will classify AI tools based on their risk levels - from minimal to limited, high and unacceptable. It also addresses concerns such as biometric surveillance, misinformation and the use of discriminatory language. While high-risk tools will not be prohibited, their usage will require significant transparency.

Parliament's priority for the AI Act - which could become law early next year - is to make sure that AI systems used in the EU are "safe, transparent, traceable, non-discriminatory and environmentally friendly. AI systems should be overseen by people, rather than by automation, to prevent harmful outcomes."

A human-centered approach

Souvick Ghosh, an assistant professor at San Jose State University's School of Information, also advocates a people-led approach to the implementation of artificial intelligence at libraries and other institutions.

"The libraries need to evolve and embrace the AI revolution to make AI and conversational systems more accessible and human-centered," Ghosh wrote in 2021. "Chatbots are already being used in several websites that handle questions from patrons, offer directional advice, and point patrons to relevant resources. Deploying conversational systems in libraries and museums will help patrons with their queries and increase accessibility."

Ensuring a benefit to the community

What best practices can information professionals follow when deciding how to incorporate these emerging technologies into library operations? The Urban Libraries Council leadership brief we cited above includes the following five suggestions:

1. Move from informing to practicalizing such as leveraging AI tools for performance improvements at branches and in program design.

2. Leverage prompt engineering skills of library professionals who are already trained to find bodies of information using keywords or phrases.

3. Advance information literacy of staff and patrons through hands-on use of AI, such as workshops where participants learn how to generate short stories and poetry with AI.

4. Create an AI-focused digital inclusion network to ensure equitable access to the economic benefits of new AI powered technologies.

5. Advocate for the responsible use of AI to ensure these advancements benefit the community at large, and that foundational standards of education, innovation and access to information are preserved.

Library professionals will take the reins

Artificial intelligence and machine learning will only continue to get more advanced, but librarians are just as important as they have ever been - even if their role is evolving along with the high-tech tools.

On the American Journal Experts website, Charla Viera summed this continued evolution up nicely:

Just as library professionals took the reins in developing the search engines and strategies necessary to maneuver through intricate cataloging systems, they will also participate in the design of artificial intelligence-powered knowledge discovery tools. They will then take the initiative to educate the public on how to locate and interact with these AI tools.

Futurist Ray Kurzweil once described the librarian as "the natural leader for the age of information that is now unfolding." In March of 1993 (over 30 years ago!), Kurzweil wrote a column for Library Journal called "The Changing Library". In it he talked about what the library of the future might look like.

Why we will always need librarians

"With books in virtual form, transmitted readily through the communications ether, the emerging virtual library will not need to be housed in a building," Kurzweil wrote. "Computers will continue to facilitate the efficiency of administration, but this human-directed function will not go away.

"The last two categories of library service - knowledge finding and pedagogy - will become the primary focus of the librarian. Although computers will provide competition in these roles, until computers are capable of matching our intelligence, the role of humans in the process of managing and imparting knowledge will remain central."

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Practical, responsible and human-centered: the future of AI in libraries

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Libraries have always been guardians of knowledge, and now, with the integration of AI, they're poised to revolutionize the way we access information.

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Digital Literacy Skills to Succeed in Learning and Beyond | Yimin Yang | TEDxYouth@GrandviewHeights

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Digital Literacy Skills to Succeed in Learning and Beyond | Yimin Yang | TEDxYouth@GrandviewHeights

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We're hitting a tipping point for artificial intelligence: With ChatGPT and other AI models that can communicate in plain English, write and revise text, and write code, the technology is suddenly becoming more useful to a broader population of people. This has huge implications. The ability to produce text and code on command means people are capable of producing more work, faster than ever before. Its ability to do different kinds of writing means it's useful for many different kinds of businesses. Its capacity to respond to notes and revise its own work means there's significant potential for hybrid human/AI work. Finally, we don't yet know the limits of these models. All of this could mean sweeping changes for how - and what - work is done in the near future.

Less than two weeks ago, OpenAI released ChatGPT, a powerful new chatbot that can communicate in plain English using an updated version of its AI system. While versions of GPT have been around for a while, this model has crossed a threshold: It's genuinely useful for a wide range of tasks, from creating software to generating business ideas to writing a wedding toast. While previous generations of the system could technically do these things, the quality of the outputs was much lower than that produced by an average human. The new model is much better, often startlingly so.

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We're hitting a tipping point for artificial intelligence: With ChatGPT and other AI models that can communicate in plain English, write and revise text, and write code, the technology is suddenly becoming more useful to a broader population of people. This has huge implications.

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How Nations Are Losing a Global Race to Tackle A.I.’s Harms

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Alarmed by the power of artificial intelligence, Europe, the United States and others are trying to respond — but the technology is evolving more rapidly than their policies.

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Developing a library strategic response to Artificial Intelligence

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The purpose of this working document is to set out considerations relevant for libraries developing a strategic response to Artificial Intelligence.

The text is organised around developing a set of questions that prompt reflection and action (section 4). It is hoped that the document can support local decision making about AI.

Authorship: This working document for discussion was prepared by Andrew Cox, as convenor of the Artificial Intelligence SIG. Comments for further iterations of the document are invited (link to comment form - if you have difficulty accessing this form send comments to a.m.cox@sheffield.ac.uk).

A note on methodology

Initial ideas for the report were derived from an event run at the University of Sheffield in April 2023. An early version of this document as a working paper for comment was published on 4 June 2023. We would like to thank everyone who added thoughts and comments to this draft. We have also drawn in some data from a survey (N=111) linked to the Sheffield event and recirculated in July; respondents were a mix of HE and FE librarians and health librarians. As the audience was primarily in the UK this data should be understood as simply a snapshot of opinion in one context.

Version 1.1 20 November 2023

A pdf version can be accessed @ https://doi.org/10.15131/shef.data.24631293.v1

Definitions of AI typically revolve around the idea of computers performing tasks which are ordinarily undertaken using human intelligence. The UNESCO definition emphasises that this is imitation of human understanding.

"Machines that imitate some features of human intelligence, such as perception, learning, reasoning, problem-solving, language interaction and creative work (UNESCO, 2022: 9).

More expansively, UKRI (2021:4) define AI as

"a suite of technologies and tools that aim to reproduce or surpass abilities in computational systems that would require 'intelligence' if humans were to perform them. This could include the ability to learn and adapt; to sense, understand and interact; to reason and plan; to act autonomously; or even create. It enables us to use and make sense of data."

The EC definition emphasizes the importance of data.

"Simply put, AI is a collection of technologies that combine data, algorithms and computing power." (European Commission, 2020: 2)

AI is not new. We are already rather familiar with many of its applications in auto-suggestion, spam filtering, plagiarism detection, audio transcription, text summarisation and translation. Many familiar features of search and recommendation use AI. More specifically in the library context, Text and Data Mining (TDM), and the application of machine learning to library and archive collections in the digital humanities can be seen as AI.

While controversial there are many beneficial uses in every area of human activity. More specifically, AI is positive for access to information and knowledge. For example, improving translation tools enhances access to material written in other languages. Improved summarisation also makes access to content easier.

The most powerful applications of AI for libraries are of "descriptive AI" which can be used to make all kinds of material (photos, videos, sound, manuscripts) in collections machine readable data through such techniques as computer vision or sound to text, and provide description at scale for information retrieval (Cordell, 2020). Some libraries have special collections that could be made more accessible using these means; for others it may be more relevant to have access to an infrastructure around licensed or ) open content. Many technical challenges remain with digitization and attempts to automate description of historic collections. But there is already considerable experience of the issues especially in the national library and archive community (Lee et al., 2023):

Although AI promises to enhance access to knowledge, there are serious ethical concerns in the areas of bias; privacy protection; explainability, transparency and accountability; and social impact (Jobin et al., 2019; AIAAIC, 2023). These apply strongly in the context of AI developed by Big Tech companies, but may be managed in the context of library specific applications of AI (Padilla, 2019).

The release of ChatGPT has led to a surge of interest in AI and also a re-evaluation of how it is defined and the anticipated professional implications. Generative AI has shown remarkable ability to write all genre and styles of text, write code and generate images in response to prompts. The underlying technologies in themselves can be leveraged by libraries, e.g. Large Language Models like GPT can be trained with library selected data. The issues lie more with the commercial drivers that have shaped the development of tools such as ChatGPT. The informational and ethical issues around ChaGPT illustrate many of the issues posed by all AI, because it (IFLA AI SIG, 2023b):

makes biased statements, e.g. reproduces biased assumptions about gender and politics (Motoki et al., 2023; Deshpande et al, 2023)

"hallucinates" information which is inaccurate

fails to acknowledge its sources or even invents sources

threatens to accelerate the uncontrolled creation of content and can be used to create fake news, to manipulate and polarise public opinion, spread misinformation and undermine democracy, or even incite violence

may violate copyright by using text and data without permission (Dreben, 2023). Few LLM providers have made details of the training data they have used openly available

is unexplainable because it is not open about what data it is based on or how it works

threatens human jobs, e.g. journalists and those working in marketing

is available to people with money to subscribe, disadvantaging those without, and so deepening digital divisions

was developed by exploiting very low paid Kenyan workers to detoxify content, an instance of the dependence of AI on precarious, ghost labour (Perrigo, 2023)

has significant environmental impacts (Burruss 2020; Ludvigsen, 2022; Saenko, 2023)

reveals the disruptive power in the hands of Big Tech companies and the dizzying speed of change it seems to enable

The implication for the library world is to increase the importance of AI literacy training, as opposed to applying AI to library work itself.

AI has the potential to have "wide and deep" impacts on library work.

From table 1 below we can see AI impacting many library services, sometimes changing them fundamentally, but others only making marginal changes. It is logical to anticipate that libraries will adopt AI in ways which either align to existing roles, strongly link to user need or demand the least resource.

We have already stressed the relevance of descriptive AI to making library collections more accessible. AI is being used to provide initial metadata for items. It is likely to appear in search services and used in supporting some dimensions of systematic reviews (e.g. filtering of results).

As more and more scholars use AI techniques in their research, so the need to support data scientist communities will grow. Libraries can offer support in terms of data discovery, copyright issues, data management and data preservation.

AI is likely to change everyday knowledge work, e.g. through translation, summarisation and text generation. A proliferation of AI tools and apps can be applied to library professional work in particular. Tools such as ResearchRabbit, Scite, elicit and openread perform tasks to support literature reviewing. Generative AI has applications in library marketing because of its ability to adapt text to the needs of specific audiences.

AI's ability to perform complex routine tasks accurately means that it is likely to be deployed in back end library systems. An example of this is use of RPA (Robotic Process Automation) to process bibliographic data.

Given the number of enquiries libraries receive, chatbots have been advocated for libraries for some time. This is increasingly plausible because of the decline in technical barriers to chatbot development. They could fulfil roles such as:

Answering routine queries

Collecting information from users

Supporting users through routine processes

Being a buddy to new students

AI will be used to create smarter library spaces. Some libraries have developed physical robots to answer user queries. Robots have also been used to perform such functions as shelving and stocktaking. Some libraries have applied Automated Storage and Retrieval System (ASRS) which retrieve book stock on demand. This usually requires a major rebuilding programme.

For educational libraries, other educational uses of AI such in creating adaptive learning content or chatbots to support student experience are somewhat relevant too (Jisc, 2023b).

Generative AI has shifted the focus of the debate because of its widespread use by users, bringing to the fore the need for staff and students to have some level of AI literacy (encompassing data and algorithmic literacies). This is a natural role for libraries extending their promotion of information literacy and digital skills. AI literacy is the understanding of AI in any of its manifestations, involving the ability "to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace" (Long and Magerko, 2020).

It seems likely that AI literacy will be essential in the future workplace; although the exact nature of skills to use/collaborate with AI are still emerging; and how this is conceived is likely to be specific to particular disciplines.

AI can also be applied to predicting patterns of user behaviour and so decision making.

Table 1 AI impacts on library operations

AI to make collections machine readable data and describe them at scale

Collections team, Special collections, Archives team

AI to enhance or create metadata

Metadata team

Discovery/retrieval, literature reviews

Library systems, liaison team

Supporting data scientist communities

Liaison team

AI generated text and images

Marketing team

Library or institutional chatbot

User services

AI in backend systems, e.g. RPA (Robotic Process Automation)

Library systems

Use of robots to give information to users

User services

Smart spaces

Facilities team

Use of robots to tidy shelves

Collections team

Supporting student use of AI tools

Academic services

Need for AI literacy (including data and algorithmic literacy) of users

Training team

Analysing and predicting user behaviour

Planning team

Results from the survey are suggestive of the level of planning/ development at the time of writing (N=111). AI literacy has rapidly moved to the fore.

Table 2 planned, pilot and mature AI services in libraries
Which applications demand least resource and align most strongly with user need and existing library roles? Which developments are most critical in reshaping the library role? Which are most likely to happen and in what time scale? How can AI technologies enhance our library services? What challenges can AI help address? What are the potential risks and ethical considerations, and how can we mitigate them? How can we continuously monitor and stay up-to-date with emerging AI trends and advancements? How can libraries teach users AI literacy effectively? What are the key learning outcomes needed, and how do they vary by discipline? How should AI literacy be integrated into existing IL, academic and digital literacy training? How can material be updated to keep up with the changing nature of AI?
In a context of change and uncertainty thinking and acting strategically is seen of increasing importance. Many institutions are placing greater emphasis on strategy, on envisioning a desired future state, and planning to realise this vision. For libraries a key issue is to position themselves strongly in relation to wider institutional, sectoral and national priorities (Cox, 2021). This can be a form of passive alignment, seeking to demonstrate the library's contribution to the organisational mission, or even proactive in seeking to take a leadership role in certain areas.

Library responses to AI happen in the context of government policy and existing and emerging legal frameworks. From around 2019 onwards, many states have recognised AI as a strategic priority. According to an analysis of these policies by Papyshev and Masaru Yarime (2023) some strong common themes emerge such as the need to:

Develop human capital

Apply AI ethically

Develop a research base

Regulate

Develop data infrastructure and policy.

One can immediately see information professionals playing a role in achieving many of these priorities, such as by educating citizens to help develop the skills for an AI literate workforce; by advocating for their unique perspective on the ethics of AI; by supporting researchers to develop the research base for AI; and by inputting on the design and use of a data infrastructure. If AI is a national priority, it seems that libraries have a significant role to play, alongside other actors.

While there are many common themes between them, the emphasis in different national policies is somewhat different. Papyshev and Masaru Yarime (2023) suggest that they fall into three groups:

Development - where the state steers development of AI towards national goals. This kind of policy is found in China and Japan, and in Russia and some of the former communist bloc in Eastern Europe.

Control - where the focus is on state regulation and protecting society from the risks of AI. This is the approach taken by the EU, for example.

Promotion - where the emphasis is on innovation, especially in the private sector, and the state plays only a facilitating role. This is the emphasis in USA, UK and other countries including Australia, Ireland and India.

These categories seem to reflect persistent patterns in the political culture in these different countries. There has probably been a shift towards regulation internationally because of the controversy around ChatGPT. This could have radical implications for how AI is developed and used in the library sector.

Libraries may also need to respond to sectoral strategy, such as around culture or health. Existing legal frameworks are still relevant, such as that for the protection of IPR.

The AI strategies of organisations within which libraries are embedded is obviously of importance. But to date AI appears to be rarely mentioned in university and academic library strategies as such (Huang et al., 2023).
How is the state's stance towards AI likely to impact library uses? What is the strategic stance of your institution and wider sector towards AI? What hooks are there in existing strategies where AI may be relevant?
There can be themes in pre-existing institutional and library strategies that offer hooks for aligning AI related activity.

Table 3 Hooks in existing strategy example: university sector
Where do we see our current strategic priorities leveraging AI? Do we want to align to the institution or proactively shape the institutional response? How do issues such as EDI and decolonisation play out within AI applications such as descriptive AI?
AI could be seen as the latest of a number of technologies that collectively offer digital transformation. Some authors refer to SMACIT technologies (social, mobile, analytics, cloud, and Internet of Things), but they could also include AI. The characteristics of these technologies are that they go beyond automation of previous practices to enabling fundamental rethinking of processes. "Digital transformation is the profound and accelerating transformation of business activities, processes, competencies, and models to fully leverage the changes and opportunities brought by digital technologies and their impact across society in a strategic and prioritized way" Demirkan et al. (2016). It is about changing competencies and organisational culture and structures as much as purely about technology.
What are the current commitments of the institution around digital transformation? Does AI shift these priorities?
SWOT

The SWOT below evaluates the strategic position of libraries in general in relation to AI
Strengths

AI often seems to be technology driven. In this context, knowledge and prioritisation of user need, which is central to library professional discourse, is an important corrective, aligning to Institute for Ethical AI in Education (2021) recommendation that AI projects being driven by learner benefit.

Many aspects of descriptive AI have been long been a focus of library work and advocacy, e.g., clearing the legal and technical obstacles to Text and Data Mining. As data science techniques are used across more disciplines so this knowledge will be increasingly relevant.

Since data is key to AI, knowledge of data management and data governance is highly relevant. Libraries have knowledge about such issues as searching for data, data description, data ownership and licensing, promotion of data sharing, and data preservation. These are all relevant to AI.

In the context of the informational and ethical weaknesses of generative AI, the trust that is invested in information from libraries is an important value. The library is a place committed to open sharing and building multi-disciplinary communities.

Understanding the biases and promoting AI literacy has continuity with library work around data and algorithmic literacy.

Other strengths relate to the nature of the profession and the structures it has for sharing knowledge and mutual learning across the sector.

The values of the profession offer important reference points, for example the commitment to access to knowledge for all challenges the tendency of commercial services to intensify digital divides; the stress on impartiality speaks to bias in AI; and privacy protection is a key concern with AI.

As a female majority profession, librarians can play a special role in counter-balancing the impacts of gender bias in the wider IT industry.

Weaknesses

Many libraries have limited in-house technical development capacity. This means that it is hard for them to run AI based projects even as the technology becomes easier to apply off the shelf.

Libraries may lack ownership of collection or user data at the scale to justify the application of AI.

Commercial products are expensive and there is still a lack of off-the-shelf products for libraries.

The reason companies like Google, Microsoft and Amazon are dominating development of AI is that they have huge resources and have acquired big data about user behaviour. Libraries are likely to have smaller amounts of data, not necessarily of the same quality, and perhaps with issues around their inclusivity. Applying algorithms trained on modern data may be less successful with historic material.

AI developments driven by Big Tech often conflict with core library values such as protection of privacy, removal of bias, access for all and openness.

The levels of professional uncertainty and anxiety around AI remain high.

If new forms of collaboration are created around AI, there will be different professionalised understandings of the benefits and purposes and the usual challenges of communicating across professional boundaries.

Ultimately there is always a competition between priorities and the way libraries have developed has tended to focus on roles such academic skills and pedagogy rather than technical innovation.

In our survey, participants perceived several barriers to using AI in their library (N varied slightly across the items).

Table 4 Perceived barriers to implementing AI
Opportunities

AI tools are improving access to knowledge, through description of content, summarisation, translation and transcription.

Generative AI can be used in many professional tasks, such as drafting documentation, communications and policies

AI can be applied to certain types of routinised tasks, e.g. creating initial metadata records for material. There is the potential for it to take up routine and create more space for higher value, skilled work.

The information weaknesses of generative AI increase the demand for trusted information.

AI developments are creating opportunities for new forms of collaborations, such as with data scientists.

AI may be used to improve decision making and prediction in libraries.

Libraries may be able to influence how systems vendors incorporate AI into their products and how wider infrastructures are developed to incorporate AI

Libraries can influence the institutional approach to engaging with AI based on library principles such as openness, privacy and explainability

If there is a library vision of how AI can enhance access to knowledge then there is an opportunity the technologies being developed can deliver it.

Threats

Media coverage of AI, generating both hype and fear, creates an environment in which it is hard to make balanced decisions.

The speed of change, particularly initiated by ChatGPT, makes it difficult for institutions to respond in a timely way.

Change is driven by actors beyond influence or control.

The lack of a diverse workforce in the AI industry linked also to the cultural associations between technology, rationality and masculinity, suggest that on balance AI is likely to negatively impact social equality, just at a time when EDI has become recognised as a more urgent priority. An excessive focus on technologies detracts value placed on the caring dimensions of professional work.

Much of the policy and advocacy around AI stresses a productivity agenda, often linked to reduced employment, and potentially to intensification of demands on staff and increased surveillance.

The most direct threat is that the way that users find and use information is changing and this could make libraries seem less relevant. For example, the ChatGPT model of receiving a fully developed answer to a question expressed in natural language challenges the keyword/ search results model of finding information.

Table 5 Summary SWOT of libraries and AI

Strengths
Knowledge of user need
Data is key to AI
Previous experience with TDM, digital humanities, copyright
Trust in libraries as information sources
Professional knowledge sharing
Professional ethics, values and skills
Track record of successful collaboration and connecting different groups within the institution
Openness and cross disciplinary nature of the library
As a female majority profession given the lack of diversity in the AI industry

Opportunities
Improved access to knowledge/ collections: through content description, summarisation, translation and transcription
Completion of routine tasks with AI
Improved knowledge creation through generative AI
Demand for trusted information
Collaboration
Better-informed decision making
Higher-value work enabled
Influencing better products from vendors
Influencing rules of engagement with AI based on library values/principles
Having a vision for AI

Weaknesses
Limited technical development capacity of libraries
Cost of commercial products
Lack of off-the-shelf products for library context
Data quality issues, lack of data, limits on use of data, biased data, non-inclusive data
Differing understanding of issues and benefits within AI driven collaborations
Uncertainty, anxiety and lack of confidence in the sector about AI
Library and professional brand not associated with AI
Potential for AI to conflict with professional values (e.g. confidentiality, privacy, equal access)
Other pressing priorities, many more closely aligned to professional identity

Threats
Emotion, hype and misinformation around AI
Speed of change, driven by exogenous actors
Ethical issues: bias, issues of privacy and confidentiality
Lack of diversity in the AI workforce
Risks attached to the productivity agenda driving AI strategy
New ways of accessing information change expectations about search etc

What is the SWOT for our library? Organisational capability
Mikalef and Gupta (2021:2) have developed a model of organisational capability for AI, the ability of an organisation "to select, orchestrate, and leverage its AI-specific resources." Rooted in the resource-based theory of the organisation this differentiates three types of resource that make up AI capability: Tangible resources, Human resources and Intangible resources (Table 1).

Table 6: Resources required for AI capability (Mikalef and Gupta 2021)

Tangible resources are data resources (like user data or collection data), having a suitable technical infrastructure, and access to "basic resources" such as money and time to invest in AI. Many libraries do have data in the form of both collections and user data. They may also have access to the necessary technical infrastructure to support AI. Funding is always a challenge, but the exciting potential of AI may make it possible to build a business case for funding.

Human resources combine both the technical skills to develop AI applications, and, equally important, the business skills to plan and deliver AI projects and implement AI as a service. Libraries may well have significant technical skills in their teams. They are used to delivering on technical projects. Given the changing technical landscape of the last few decades, there is also a huge amount of experience in libraries in implementing and promoting new systems. It is increasingly recognised that AI should be developed in participatory ways with stakeholders.

For Mikalef and Gupta's (2021) intangible resources include the ability to coordinate activities between departments, the ability to manage organizational change, and willingness to take risk. These might be seen as leadership challenges. Delivering them may also imply structural reorganisation. Again, libraries often have capabilities here, especially in terms of coordination. So much organisational change has happened in the last few years, that again the ability to adapt with agility has increased.

Mikalef and Gupta's (2021) model could be used as a framework to evaluate whether a library (and its host organization) have capacity or readiness to develop, implement and AI systems, especially descriptive AI.

Capabilities differ between sectors. National libraries, and some research libraries, have a proven track record in developing descriptive AI. Critically they have vast bodies of collection data that would benefit from AI to enable improved access. Given the benefits, they may be able to find the funds to support such projects. They can develop technical skills through proof of concept projects and develop the business skills through turning projects to services.

The case is less clear for smaller, less resourced libraries, particularly if they do not have unique collections requiring special treatment. They are more likely to license systems. This is not to dismiss the possibilities of using descriptive AI, but is more likely to be through collaboration. Here there may need to be longer term processes of capacity building, e.g. through training staff and proof of concept projects. While they may not have AI capability for themselves, they may contribute in very significant ways to the AI capability of their wider organisation, such as the university, in the case of an academic library, or a health service, in the case of health libraries.

Promoting AI literacy has a place in promoting organisational or societal AI capabilities.

The strategic responses to AI could include one or a combination of the following broad approaches:

1. Recruiting new staff with specialist AI skills

2. Upskilling existing staff

3. Engaging with users to see how they are using AI

4. Studying sector best practice

5. Running proof of concept projects

6. Talking to the system suppliers and buying systems

7. Aligning to what is happening in the institution
Collaborating with other units

8. Aligning to what is happening in the sector
Collaborating with other libraries and organisations

9. Adopting a wait and see stance

1. Recruiting new staff with AI skills

Recruiting AI-skilled staff is one option for libraries aiming to use AI. Building a skilled AI team could be critical to successfully tackling the complex technical (and implementation) challenges posed by AI. It would require a well-structured recruitment process to attract and retain suitable talent, given the current demand for AI skills and the relatively low pay in the sector. The ethos of the sector might attract workers. It remains unclear exactly what skillsets might be most useful, e.g. is it about technical or implementation skills?
What type of skills do we need to acquire through recruitment? Where would such staff sit within the organisation? Who should coordinate the library's response to AI?
2. Upskilling existing staff

Upskilling existing staff is a proactive strategy for keeping the workforce adaptable and competitive in a rapidly changing landscape. It benefits individual employees by enhancing their career prospects and strengthens the organisation by ensuring it has the talent and expertise needed to thrive. But it does place extra demands on staff who are often already hard pressed, especially with a complex topic like AI.
What types of technical, data related and business knowledge are needed? What resources are there to support this learning? How can we create space for staff to explore AI and learn relevant skills? How do we ensure that staff continuously upskill in this area to keep up-to-date given the speed of change? How can the efforts of diverse individuals be coordinated?
Some options:

Personal exploration of AI based productivity tools

Exploration of open source AI tools and apps

Reading and discussion groups

'Drip feeding' AI into team meetings/conversations

Undertake job analysis exercise - a detailed look at individual and team workflow to identify potential opportunities, then look at relevant training to upskill

Training courses

Data related skill development

The IFLA AI SIG (2023a) published a list of 23 at the beginning of the year.

Cost effective approach

Complexity of topic

Staff interest in developing new skills

Competing priorities

Lack of accredited courses or agreed syllabus

Our survey gave an impression of how respondents prioritised different types of skill development. This suggests that willingness to learn about AI basics and applications is combined with an emphasis on enduring professional skills. Core data scientist activities such as coding and statistics appear very low on the list.

Table 6 Survey views on the types of knowledge, skills and other attributes librarians most need to develop to apply AI to knowledge discovery (N=111)

3. Engaging with users to see how they are using AI

AI is rapidly evolving in our time. Notably ChatGPT is changing how users discover information and write, and so their learning (Jisc, 2023a). It is critical to engage with our users to understand how it is impacting their information behaviour.
How are our different groups of users using AI? How do we support all users to navigate the AI landscape and maximise the positive benefits of AI while using it ethically?
4. Studying best in sector practice

Studying sector best practices is about drawing insights from the experience of others to make well-informed, effective, and ethical decisions. Best practices serve as a valuable blueprint for achieving success. Learning about what has not worked, can be as valuable as discovering what has been successful. However, at the present time there do not seem to be many real world use cases to draw on.
What are comparator institutions doing? What works and what does not? What can we learn from how other related sectors are using AI for (e.g. museums, galleries, archives etc.)? What can we learn from how more distant sectors are using AI for (e.g. health, retail, transportation etc.)?
5. Running proof of concept projects

Running proof of concept projects is a valuable practice for minimising risks and maximising the chances of success in developing services, especially in areas like AI, where complex technologies and innovative concepts often require validation before full implementation. They can help claim a place at the table by demonstrating the relevance of the library. Project management is a particular skillset. Turning a proof of concept project into a service is a major challenge in itself.
What immediate projects are there that could explore the benefit at point of need? How can projects then be developed into services?

Tests the issues in a real world context

Resource demands

Minimises risk

May be hard to manage expectations created in a pilot

Builds skills incrementally

6. Talking to system suppliers and evaluating systems

Engaging with system suppliers and purchasing or licensing technology solutions is a common approach taken by libraries. It effectively outsources development costs, technical skills and risk. Due diligence is crucial to making informed decisions that align with organizational goals and deliver value to operations. There is a need for collective effort to influence the development of the marketplace. Sharing evaluation checklists might be one way to promote this.
Can our system suppliers offer us suitable tools? What is the key functionality we wish to have access to? What would be the rules of engagement for key requirements from AI in relation to transparency, data sources, bias, privacy and ownership of user data?
7. Aligning to what is happening in the organisation: Collaborating within the organisation

Aligning the implementation of new tools (and related processes) with what is happening within the wider organisation and promoting collaboration are vital for successful technology adoption. A holistic approach that considers the organisation's goals, people, processes, and culture will ensure the best result. At the same time, collaborations reduce control and create "political" and communication challenges.

Potential collaborators are:

IT services

Student facing services (if not integrated with the library)

Academic departments, especially computer science, data science and philosophy (ethics and society)

Emergent multidisciplinary networks of data scientists

Faculty deciding policy e.g. around AIED (AI in education) or AI in research

How are other departments in the institution using and responding to AI? Who can we collaborate with internally to increase our capacity and influence ? How can we gain a seat at the table to assert the relevance of our knowledge and needs? Who are the key decision makers?
8. Collaborating with partners outside the organisation

Collaborating with peer institutions enables more informed decision making and reduces risk. But establishing collaborations requires an investment of time to build trust.

Turn to existing communities that are working in the AI and library (and wider GLAM) space:
IFLA Special Interest Group on AI CENL AI in Libraries Network Group AEOLIAN network
There are, of course, many organisations beyond the library world that are important to work with.
Who can we collaborate with externally to increase our capacity and influence?
10. Adopting a wait and see stance

Given the many competing pressures on libraries a wait and see stance conserves resources. But if this approach can be beneficial in many scenarios, libraries should be mindful not to wait too long, as they risk falling behind and losing perceived relevance. Finding the right balance between cautious evaluation and timely adoption is crucial for making informed decisions about AI implementation.
Where does the library want to sit in the diffusion of AI innovation: from innovator, early adopter, early majority, to late majority or late adopter?
Given the breadth of impact of AI, there could be many strategies for libraries. But we have picked out three that seem to be important today.

Where they have large collections of unique content needing improved description for retrieval libraries can apply descriptive AI to create exemplars of ethical, responsible and explainable AI in resistance to Big Tech's offerings (Lee, 2023; Padilla et al., 2023). This can be achieved by following principles of good governance, such as through:

· Ensuring that the selection of collections to apply AI to is appropriate, taking into account technical and copyright issues, but also respecting issues of inclusivity, indigenous rights and decolonisation; Respecting the rights of those who are represented in collections and all other stakeholders; Appropriately rewarding/acknowledging volunteer and crowd workers; Respecting IPR issues, e.g. copyright in collections / licensing of content

· Making services usable, accessible and explainable to intended users

There remain many challenges with achieving this, such as how to:

Not all libraries have collections needing the use of AI, but librarians' data related expertise have high value for institutional applications of AI because today's AI is data driven. This expertise can help support data scientists across the wider organization within which the library sits, such as multidisciplinary communities data scientists in an academic context, or analysts examining data within health services or government. Relevant activities include:

· Explaining the importance of the provenance, validity and quality of that data to understand how it can be appropriately used

· Explaining what data can be used for and what not according to copyright, IPR etc

All these practices align to professional knowledge of information governance and stewardship, but there is a need to translate this knowledge to the domain of data.

The strategy most aligned to existing library practices and librarian identities, particularly in university, school and public libraries, is to take a lead role in promoting AI literacy. There is a widespread understanding that the public, as citizens and workers need to understand the new technologies. Students, whatever discipline they are studying, need such knowledge for employability.

Librarians have already developed information literacy offerings, and many dimensions of AI literacy could be folded within these. They have developed the pedagogic knowledge and skills needed.

AI literacy is likely to include the ability to identify when AI is being used; to appreciate the differences between narrow and general AI; to understand what types of problem AI is good at solving; to understand how machine learning models are trained. It would also include awareness of ethical issues such as bias, privacy, explainability and social impact.

Since AI is based on data, data literacy is recognised to be a component of AI literacy. Algorithmic literacy is a concept that has already been developed to describe awareness of how services such as search and recommendation are increasingly shaped by algorithms to personalise and adapt content, but also can limit the visibility of information and create filter bubble effects. More formally it has been defined as "being aware of the use of algorithms in online applications, platforms, and services, knowing how algorithms work, being able to critically evaluate algorithmic decision-making as well as having the skills to cope with or even influence algorithmic operations" (Dogruel et al, 2022: p.4). Extending algorithmic literacy beyond the context of search is relevant to AI literacy.

AI is complex and hard to explain. It has multiple applications and guises. It is based on hard to understand computational ideas and statistics. Often the outcomes of decisions made by AI are hard to understand even for its designers because the machine learns patterns from data. While some images of AI lead us to expect to go to a service that is explicitly AI (as with ChatGPT), in fact it is often embedded in an infrastructure, so it is not easy to recognise or resist it's working. Indeed, it would be fair to say that BigTech does not necessarily want how AI works to be known, because it's a commercial secret.

AIAAIC (2023). AI, Algorithmic, and Automation Incidents and Controversies Repository https://www.aiaaic.org/

Cordell, R. (2020). Machine Learning + Libraries: A report on the state of the field. https://labs.loc.gov/static/labs/work/reports/Cordell-LOC-ML-report.pdf?

Cox, AM (2021) The impact of AI, machine learning, automation and robotics on the information professions: A report for CILIP https://www.cilip.org.uk/general/custom.asp?page=researchreport

Cox, AM (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.

Deshpande, A., Murahari, V., Rajpurohit, T., Kalyan, A., & Narasimhan, K. (2023). Toxicity in chatgpt: Analyzing persona-assigned language models. arXiv preprint arXiv:2304.05335.

Dogruel, L., Masur, P., & Joeckel, S. (2022). Development and validation of an algorithm literacy scale for internet users. Communication Methods and Measures, 16(2), 115-133.

EuropeanaTech AI in relation to Glams taskforce (2021). Report and recommendations. https://pro.europeana.eu/project/ai-in-relation-to-glams

Huang, Y., Cox, A. M., & Cox, J. (2023). Artificial Intelligence in academic library strategy in the United Kingdom and the Mainland of China. The Journal of Academic Librarianship, 49(6), 102772.

IFLA AI SIG (2023a). 23 resources to get up to speed on AI in 2023 - selected by the IFLA Artificial Intelligence SIG https://www.ifla.org/g/ai/23-resources-to-get-up-to-speed-on-ai-in-2023/

IFLA AI SIG (2023b). Generative AI for library and information professionals (draft), https://www.ifla.org/generative-ai/

Jisc (2021). A pathway towards responsible, ethical AI. https://beta.jisc.ac.uk/reports/a-pathway-towards-responsible-ethical-ai

Jisc (2023a) Student perceptions of generative AI, https://beta.jisc.ac.uk/reports/student-perceptions-of-generative-ai/

Jisc (2023b) Artificial intelligence (AI) in tertiary education. 3 rd edition. https://beta.jisc.ac.uk/reports/artificial-intelligence-in-tertiary-education

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature machine intelligence, 1(9), 389-399.

Lee, B. C. G. (2023). The "Collections as ML Data" checklist for machine learning and cultural heritage. Journal of the Association for Information Science and Technology.

Long, D., & Magerko, B. (2020, April). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-16).

Ludvigsen, Kasper. 2022. The carbon footprint of Chat GPT. Last updated December 21, 2022. https://towardsdatascience.com/the-carbon-footprint-of-chatgpt-66932314627d

Motoki, F., Pinho Neto, V., & Rodrigues, V. (2023). More human than human: Measuring chatgpt political bias. Available at SSRN 4372349.

Padilla, T. (2019). Responsible operations: Data science, machine learning, and AI in libraries. OCLC. https://doi.org/10.25333/xk7z-9g97

Padilla,T.,Scates Kettler,H.,Varner,S.,& Shorish,Y.(2023).Vancouver Statement on Collections as Data.Zenodo. https://doi.org/10.5281/zenodo.8341519

Perrigo, B. 2023. "Exclusive: OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic." Time, January 18, 2023. https://time.com/6247678/openai-chatgpt-kenya-workers/

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Developing a library strategic response to Artificial Intelligence

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The purpose of this working document is to set out considerations relevant for libraries developing a strategic response to Artificial Intelligence. The text is organised around d...

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YANG. Y., 2022. Digital Literacy Skills to Succeed in Learning and Beyond. [online]. TEDxYouth@GrandviewHeights. Available from: https://youtu.be/GgEGbMPJcAo?si=jzjNZd4UaOiG2bmp [Accessed Date 29 October 2023].

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CORDELL, R., 2020. Machine Learning + Libraries A Report on the State of the Field. [online]. Library of Congress Blogs. Available from: https://labs.loc.gov/static/labs/work/reports/Cordell-LOC-ML-report.pdf?loclr=blogsig [Accessed Date 1 December 2023].

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Yimin Yang is a 13-year-old student at Southlands Elementary with a passion for public speaking. TEDxYouth provided him with the platform to share his ideas on the importance of digital literacy skills. Although not as accredited as other sources in the bibliography, Yang’s age means he has a unique perspective as a digital native. However, states that although the use of devices may come naturally, guidance is critical. His engaging communication style, storytelling and analogies demonstrate an exemplary understanding of digital literacy skills. Notably, when he explains that browsing online isn't a one-way communication. Users cannot see or control how their data is being shared. Yang compares this to his audience, as they can see him but not the crew that watches them from backstage. To conclude, Yang’s skills and TED Talks process of reviewing their speakers would suggest this is a credible source.

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SATARIANO, A., et al., 2023. How Nations Are Losing a Global Race to Tackle A.I.’s Harms. [online]. The New York Times: Available from: https://www.nytimes.com/2023/12/06/technology/ai-regulation-policies.html [Accessed Date 4 December 2023].

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IFLA. 2023. Developing a library strategic response to Artificial Intelligence. [online]. IFLA. Available from: https://www.ifla.org/developing-a-library-strategic-response-to-artificial-intelligence/ [Accessed Date 1 December 2023].

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This Report provided by the IFLA is the most recent version that was circulated in November 2023. It contains that developed on the previous reports created in June and July 2023 as a "strategic response to Artificial Intelligence" (IFLA 2023). The report delves into great detail on the subjects from definitions of AI, how AI has impacted libraries supplying tables to support statements and adopting SWOT to create a well-considered strategic plan to improve infrastructure and seek funding and training to equip staff. The report outlines responses in research and table formats to develop three strategies. One discusses how outlining implications to form models for libraries to create responsible and explainable applications. Two use library data to investigate where AI capabilities are best suited. The third strategy is to promote AI literacy to the extent that tech companies allow to make AI more accessible to the public.

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The online article from The New York Times by authors Adam Satariano and Cecilia Kang is the third article in a series that tackles the subject of modern artificial intelligence. The article is dense with information and contains multiple links to support the order of events and steps taken whilst simultaneously delving into an overview of rapid recent AI developments, the initiatives, and policies that the European Union have implemented in an attempt to keep up with the changes. However, government policies simply cannot evolve quickly enough to stay ahead of technology. This is partially true that the tech companies such as Open AI have been entrusted to develop safely and consciously however this is due to a lack of AI literacy in the government bodies. The New York Times is a reputable and credible source and has dealt with dense topics effectively and clearly.

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PRESSREADER. 2023. Practical, responsible and human-centered: the future of AI in libraries.  [online]. PressReader. Available from: https://blog.pressreader.com/libraries-institutions/practical-responsible-and-human-centered-the-future-of-ai-in-libraries [Accessed Date 30 November 2023].

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This report by Ryan Cordell, Associate Professor of English at Notherneastern University, was commissioned by the Library of Congress as part of their initiative "Invest In Our Future" as part of a digital strategy. The report is just short of one hundred pages and boasts an extensive, varied and rich list of 104 works cited. The article topic tackles the topic of the allure of Machine Learning and how the capabilities of the tool may threaten the role of libraries. From peer-reviewed articles, reputable magazine articles, interviews and more, the variety of the research conducted to support each subject gives this report research that surpasses most other sources in this bibliography. Yet despite the volume of the report, it is well-documented and maintains good readability. The Cordell, author of the report is highly-accredited with his work on reports for digital libraries and scholarly infrastructure, making this report a credible source.

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MOLLICK, E., 2022. ChatGPT Is a Tipping Point for AI. [online]. Harvard Business Review. Available from: https://hbr.org/2022/12/chatgpt-is-a-tipping-point-for-ai [Accessed Date 29 October 2023].

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This journal article by Lai supported by the American Library Association, evaluates how Chatgpt handles reference inquiries based on question types and complexities. The author discusses how the topics of Gen AI Tools are handled in the media, specifically Chatgpt. Using literature reviews, methodologies, table references, about various queries put to the AI tool adhering to the article's objective effectively. In conclusion the strength of the AI tool lies in its highly trainable nature, which makes it useful for convenient and repetitive instructions in responding to queries and complaints. The tool maintains a consistently professional and gorgeous manner with formal layouts, proper salutations, and signature lines. However, the tool has weaknesses, such as not being able to detect nuances and being unable to make proper referrals to other units to search outside its pre-ingested training data. The data collected and the extensive reference list would support that this is a credible source.

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The following online article by Mollick explores the risks and opportunities that generative AI presents. The author provides an overview of the extensive abilities of AI and the advantages and opportunities for businesses and the public alike. However, as the title implies, the article focuses on the imbalance, so to speak. How AI is advancing at such an alarming rate, that as much as the known implications are problematic for the user, the biggest potential threat is how little is known about AI's full capabilities. In summary, the article makes strong arguments and the author's work is highly cited within academic databases, however the Harvard Business Review is a magazine.Therefore credibility as a source would not be to the same standard as that of a peer-reviewed academic journal. However, it should be noted that online content, unlike academic sources, is accessible to the general public.

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This blog post was created by the PressReader team, the library app boasts an expansive collection of magazines, journals and newspapers. This particular blog aims to discuss how AI in the future of libraries can maintain a human-centred focus. Discussing opportunities and threats libraries face in the future, especially since AI has been integrated into many aspects of our everyday lives. It also explores how AI can be trained to complete a host of tasks to enhance the role of librarians as well as patrons' experience. In terms of threat, the blog does provide an overview of risks that would come with the convenience the technology can provide. The blog contains several links that support the topics discussed and display suggested articles they have created based on similar topics. Based on this and the fact that PressReader is a legitimate company verifies the credibility of the source.