Selected by the IFLA Artificial Intelligence SIG

Artificial intelligence is said to be having a dramatic, even transformative effect on many aspects of society. It will affect libraries in multiple ways, through its use in library services but also through changing the search landscape, and so information literacy. We in the IFLA Artificial Intelligence SIG feel AI is something every professional is curious about. Therefore we have prepared the document below, selecting 23 resources for getting up to speed on the topic. We have selected items which are up-to-date and openly available.

In December 2022 we made this and updated it slightly in December 2023. See our other guide to Generative AI for library and information professionaIs for more about the new face of AI in 2023.

We are very much aware that the list is heavily biased towards English language resources: we are actively looking for resources in other languages.

The 23 resources

A. Starting points: What is AI?

  1. Elements of AI, [A free online course explaining the basics of AI technologies. It is translated into a number of European languages. There is also an Ethics of AI course: ]
  1. AI for anyone, [Includes learning materials that can be reused]
  1. CNAM “L’intelligence artificielle pour TOUS !” [A french language MOOC]

B. Applying AI to library work

Bi. General

  1. Cox, A. (2021). The impact of AI, machine learning, automation and robotics on the information professions: A report for CILIP [A review of the potential impact of AI on the information profession.]
  1. Upshall, M. (2022). An AI toolkit for libraries. Insights 35: 18. DOI: [An overview of AI illustrated with examples of AI applications and a toolkit for evaluating AI tools.]

Bii. Machine learning and library collections 

  1. Cordell, R. (2020). Machine Learning + Libraries: A report on the state of the field. [An authoritative analysis of the issues around the application of machine learning to knowledge discovery in libraries.]
  1. EuropeanaTech AI in relation to Glams taskforce (2021). Report and recommendations. [A survey of AI applications across GLAMs sector. Also includes an analysis of current barriers.]
  1. The AI4LAM community. [A community of research libraries actively developing AI applications.]
  1. Collections as data: Part to whole. [Project to promote responsible implementation and use of collections as data]
  1. Lee, B. (2022). The “Collections as ML Data” Checklist for Machine Learning & Cultural Heritage. Preprint. [Develops a checklist for the whole cycle of using machine learning with heritage material.]

Biii. Chatbots

  1. San Jose State University Library. What are Chatbots? [An explanation of what a chatbot is, how it can be developed for a library setting and with a link to a local example]
  1. Ehrenpreis, M., & DeLooper, J. (2022). Implementing a chatbot on a library website. Journal of Web Librarianship, 1-23. [case study of developing a library chatbot]

Biv.Living systematic reviews

  1. Cochrane Collaboration Living systematic reviews, [AI tools can assist in updating systematic reviews as new evidence becomes available: this could be referred to as a living systematic review.]

C. Hands on with the technologies

  1. Explore AI, [A website containing demos of different types of AI. Playing around with these tools gives you a good sense of the kind of thing that might be possible.]
  1. The carpentries, [AI is premised on data. Data carpentry provides training on core skills in use of data. There are also some machine learning introductions for GLAM under development – see e.g. Intro to AI for GLAM]

D. AI (including data) literacy training

  1. Ridley, M., & Pawlick-Potts, D. (2021). Algorithmic Literacy and the Role for Libraries. Information Technology and Libraries, 40(2). [Explains two ways that libraries can contribute to understanding of AI: through including it in IL training and through helping to produce explainable AI.]

E. Responsible AI and its ethics

  1. IFLA. (2020). IFLA Statement on Libraries and Artificial Intelligence.
  1. Padilla, T. (2019). Responsible operations: Data science, machine learning, and AI in libraries. OCLC. [An influential report on how to do AI responsibly.]
  1. Practical data ethics. [An online course explaining data ethics].

F. Strategic context

  1. Collett, C., Gomes, L. G., & Neff, G. (2022). The effects of AI on the working lives of women. UNESCO. [An important report pointing out ways to influence the inclusivity of developments around AI.]
  1. OECD AI Policy observatory, [Listing of international policy on AI. Available in multiple languages.]
  1. World Economic Forum  – Artificial Intelligence [Where to read up on global news and analysis across the AI and robotics domains]
  1. Human Centred Artificial Intelligence (2022). Artificial Intelligence Index Report 2022, [Fifth in series of annual reports on global AI developments]

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Version 2 17/12/2023