Three prompts for ‘AI in libraries’ from SCONUL in May 2022

I’ve been meaning to write this post since May 2022, when I was invited to present at a SCONUL event on ‘AI for libraries’. It’s hard to write anything about AI that doesn’t feel outdated before you hit ‘post’, especially since ChatGPT made generative AI suddenly accessible interesting to ‘ordinary’ people. Some of the content is now practically historical but I'm posting it partly because I liked their prompts, and it's always worth thinking about how quickly some things change while others are more constant.

Prompt 1. Which library AI projects (apart from your own) have most sparked your interest over recent years?
Library of Congress 'Humans in the Loop: Accelerating access and discovery for digital collections initiative' experiments and recommendations for 'ethical, useful, and engaging' work

Understanding visitor comments at scale – sentiment analysis of TripAdvisor reviews 

Various 'machines looking through documents' research projects, including those from Living with Machines – reading maps, labelling images, disambiguating place names, looking for change over time

Prompt 2. Which three things would you advise library colleagues to consider before embarking on an AI project?

  1. Think about your people. How would AI fit into existing processes? Which jobs might it affect, and how? What information would help your audiences? Can AI actually reliably deliver it with the data you have available?
  2. AI isn't magic. Understand the fundamentals. Learn enough to understand training and testing, accuracy and sources of bias in machine learning. Try tools like
  3. Consider integration with existing systems. Where would machine-created metadata enhancements go? Is there a granularity gap between catalogue records and digitised content?

Prompt 3. What do you see as the role for information professionals in the world of AI?
Advocate for audiences
• Make the previously impossible, possible – and useful!

Advocate for ethics
• Understand the implications of vendor claims – your money is a vote for their values
• If it's creepy or wrong in person, it's creepy or wrong in an algorithm (?)

'To see a World in a Grain of Sand'
• A single digitised item can be infinitely linked to places, people, concepts – how does this change 'discovery'?

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