Category: Museums, libraries and archives

AI and Machine Learning in Libraries: Promising, But Not Ready Yet

I've turned a version of the talks I've been doing on 'AI in libraries' for the past few years into an article for a library magazine. This is very much a pre-print, but it'll serve to capture a moment in time. Apparently it's an 11 minute read.

Artificial Intelligence has become one of the most discussed technologies of our time, but what does it actually mean for libraries and other cultural heritage institutions? In a decade working in digital scholarship at the British Library, I've witnessed firsthand the potential for AI to transform access to our collections – while also learning about its very real limitations.

Understanding AI: Beyond the Hype

Before exploring what AI can and can’t do for libraries, it's worth defining what we mean by AI. The term encompasses several related technologies that have evolved over time. Several years ago we talked about digital research with big data, then we were excited about machine learning and data science methods for developing software that was able to complete more complex tasks. We experimented with assistive or discriminative machine learning tools that could transcribe handwritten text, predict tags for images, detect entities like people, places and dates in text, or correct your spelling. These tools were useful, but relatively narrow in scope.

Now we have generative AI tools that can produce plausible new texts, images, and videos. However, it's crucial to understand their limitations: they can't count or do mathematics reliably, they don't truly understand words or concepts, they can't grasp real-world physics, and they can't determine truth.

I often use an image I generated with an AI tool using the prompt ‘rare books and special collections’ to illustrate this point. The result looks impressive – it has all the visual elements we associate with ancient books, including leather binding and aged paper. But when you examine it closely, it's a physically impossible ‘book’. The pages can't be opened; the text can't be read. It captures the superficial appearance of a rare book without any of the characteristics of a physical book. This perfectly demonstrates both AI's impressive capabilities and its fundamental limitations.

Figure 1 An image generated by AI with the prompt ‘rare books and special collections’.

Or to put it more flippantly, we call it 'machine learning' when it works, and 'AI' when it doesn't.

Practical Applications in Library Collections

Despite these limitations, AI and machine learning offer significant benefits for libraries. One of the most powerful applications for libraries is automatic text transcription from images and audio. A whole world of possibilities opens up once you have digital text.

Librarian and developer Matt Miller demonstrated what is possible with an early version of GPT: he took digitised images of a handwritten historical diary, and used AI to automatically transcribe the text, generate summaries of each entry, extract dates and locations, and identify people and places mentioned within. This transforms previously inaccessible handwritten documents into searchable, structured data which can be used in research or to find related items. Entity detection and linking – identifying people, places, dates, and concepts within text – allows us to create rich connections across our collections.

Object detection in images is another breakthrough. AI can identify items in photographs and generate keywords, labels, and descriptions. This technology is probably already working on your smartphone – you can search your photos for concepts like ‘dog’ or ‘dinner’, or select, copy and paste text from a photo. For libraries, this means our visual collections become much more discoverable.

Perhaps most importantly for library users, AI can cluster similar images and words, enabling conceptual rather than keyword-based searching. Instead of users needing to know how library catalogues work, they can search for ‘vibes’ or concepts, making library collections far more accessible to diverse audiences.

We can also translate text and speech into other languages – if you attended my talk at Knihovny současnosti 2025, then you probably saw the live transcription and translation of my Australian-accented English into Czech subtitles. AI can also rewrite content for different audiences, perhaps explaining complex research for tourists or children.

As an example – the process of writing this article was augmented by AI/machine learning tools. I recorded my rehearsal and live delivery of my conference presentation, then copied the text transcriptions my phone generated from those recordings into an AI tool (Claude AI). I asked the tool to turn my spoken words into an article, then edited the result into the article you’re reading now. It’s all my own thoughts, but with a level of polish that it would have taken me a lot longer to produce.

Building Institutional Capacity

The British Library's experiments with machine learning and AI didn’t happen overnight. We've invested in training and digital literacy around AI and machine learning for over a decade as part of our broader digital scholarship programme. This long-term commitment has made an enormous difference in our staff's ability to undertake experiments and be effective collaborators.

When researchers or external partners approach us with digital research projects, our staff have a sense of what might be involved, ideas for improvement, and suggestions for avoiding common pitfalls. In addition to the extensive skills required for their own jobs, this knowledge comes from hands-on experience with specific tools to understand both their capabilities and limitations, and most importantly, learning about the substantial work required to prepare data for these systems.

As we used to say in data visualisation and data science, 80% of the work is cleaning and preparing data, while only 20% is the exciting analytical or work. This remains true for AI applications in libraries.

Real-World Experiments and Collaborations

Our institutional capacity has enabled numerous experiments. For example, computational linguists analysed web archives to track how language evolves over time. For example, the term ‘blackberry’ shifted from being associated with fruit to smartphones and keyboards during BlackBerry's market dominance, then reverted as the company declined. Similarly, ‘cloud’ transformed from a meteorological term to a computing concept.

Elsewhere, Library staff developed machine learning models to detect mislabelled images in our digitised manuscript collections and created systems to identify when digitised images are upside down so they can be automatically corrected. One particularly successful project used machine learning to identify languages on title pages of digitised books. Combined with crowdsourcing verification through the Zooniverse platform, this work added 141 previously unidentified languages to our catalogue.

Our work with automatic text recognition (ATR) spans printed materials using optical character recognition (OCR), handwritten materials using handwritten text recognition (HTR), and hopefully in the future, speech-to-text for audio collections. Colleague Adi Keinan-Schoonbaert has focused particularly on extending these capabilities to non-English and non-Roman scripts – Arabic, Japanese, and other languages that aren't as well-resourced as English in current AI systems. Our work has shown how important it is to understand work you seek to automate – we talk to staff across the Library to understand their processes.

Case Study: Living with Machines

The Living with Machines project represents our most ambitious exploration to date of AI's potential for historical research. As principal investigator Ruth Ahnert described it, this was simultaneously ‘a data-driven history project and a historically informed data science project’.

We chose the name ‘living with machines’ deliberately. In 2017-2018, we realised we were on the cusp of a machine learning-led transformation that would eventually fundamentally change society. The project's meta-nature involved using 19th-century texts, newspapers and maps to understand how mechanisation transformed that era, thereby helping us understand our own relationship with emerging technologies.

British Library staff initiated the project in part to understand how machine learning was going to transform what library professionals and researchers could do with collections. We wanted to understand what AI could do well, and where it was likely to fail. The project also allowed us to explore some of the complex copyright issues around computational access (including the vital role of the text and data mining exception) and the biases potentially introduced through the selection of items for digitisation.

This massive undertaking involved over 40 people across its lifetime, typically 20-25 simultaneously. The scale reflected both the enormous collections we were analysing – millions of digitised newspaper pages and books – and our ambition to understand what happens when you bring together humanities scholars, library professionals, software engineers and data scientists to solve complex problems.

The project produced remarkable bespoke tools that responded to the challenges of working with digitised sources at scale. Place names can be slippery – it’s important to distinguish between different locations with the same name worldwide and understand when ‘Brussels’ refers to EU governance versus the physical city, so the toponym resolution system, T-Res, could identify and disambiguate place names in text. Other team members developed methods for tracking individuals across census decades, allowing them to understand how occupations changed over time.

Linguistic analysis revealed how machines were given human-like agency in historical texts. We combined crowdsourcing and vector databases to examine how mechanisation changed the meanings of words like ‘trolley’ and ‘cart’ as railways and automobiles transformed transportation.

Additional work included developing tools for searching through poor-quality optical character recognition, understanding potential biases in our digitised newspaper corpus, and pioneering computer vision approaches for reading historical maps and extracting semantic information from cartographic symbols. Impressively, the tool designed to search across Ordnance Survey maps has been adopted by scientists outside the project.

The Current Landscape: From Custom to Commodity

The AI landscape has evolved dramatically since we began Living with Machines. Wardley Mapping proposes that technologies pass through successive ‘evolution’ stages. The Living with Machines project operated during the ‘custom-built’ stage, when AI and data science tools were experimental and unique, requiring specialist expertise to build. This also allowed the project to co-create bespoke tools with humanities scholars.

Today it is much more likely that libraries can find existing products that meet their needs for common tasks like text and speech recognition, detecting objects in images, keyword search expansion, and even suggesting subject headings. Tools like ChatGPT and other large language models have made tasks possible now that were impossible in the early years of Living with Machines. Library professionals can experiment with AI tools on their desktop to prototype bespoke workflows and tasks.

Challenges and Limitations

However, we don't yet have the AI that libraries truly need and deserve. Current machine learning models embed prejudices – particularly racism, sexism, and structural inequalities expressed in historical training data. They don't represent all cultures or historical periods equally, and they often reflect commercial rather than cultural heritage values.

Ethical questions surround how training data was obtained, with numerous ongoing legal cases addressing these concerns. Environmental costs can be substantial, and companies aren't always transparent about water usage and carbon footprints.

Most critically for libraries – institutions that prize accuracy and precision – we know AI-generated content contains errors, but we can't predict where they'll occur. Even with error rates as low as 5-10%, we must carefully consider where mistakes are acceptable. Less precise keywords might be fine for discoverability, but errors in authoritative catalogue records are more problematic.

Since we often need to manually check everything or conduct sample validation anyway, it's sometimes unclear how much time these tools really save.

Looking Forward: Community and Continuous Learning

Despite these challenges, AI technologies continue advancing rapidly. What seems impossible today may be routine in a year's time.

Of course, this can make it hard to keep up with changes in the field. I encourage everyone to engage with communities like AI4LAM (Libraries, Archives, and Museums), which hosts regular online calls about different topics and maintains extensive archives of previous sessions on YouTube. They also provide Slack channels and a mailing list for ongoing conversation. These resources offer invaluable insights into how different organisations tackle specific challenges and enhance their collections.

British Library staff, particularly my colleague Nora McGregor, helped develop Digital Scholarship & Data Science Topic Guides for Library Professionals (DS Topic Guides). These topic guides were written specifically for busy librarians to provide quick topic overviews and point toward additional resources. We welcome contributions from the broader community.

Conclusion

AI and machine learning offer genuine opportunities to transform how we work with library collections, making them more accessible, discoverable, and useful for researchers and the public. However, success requires understanding both the potential and limitations of these technologies.

The key lies in building institutional capacity through training and experimentation, working collaboratively with technical experts, and maintaining critical awareness of biases and limitations. Most importantly, we must remember that AI works best when it augments human expertise rather than replacing it.

As we continue living with these machines, the goal isn't to automate everything, but to amplify our capabilities while preserving the values and standards that make libraries essential cultural institutions. The future of AI in libraries will be written by practitioners who understand both the technology and the unique mission of libraries in preserving and sharing human knowledge.

Cultures of collective work and crowdsourcing?

Nearly ten years ago I was in Estonia for the 'Community Involvement in Theme Museums' conference where I had the chance to meet and hear from people working in Estonian, Finnish and Lithuanian memory institutions. I was there to talk about 'niche' projects in crowdsourcing, so I had a few conversations about crowdsourcing in the Baltic region.

I was told that while Lithuania doesn't have a volunteering culture, Estonia has a tradition of 'talgu', or communal work, as evidenced by talks on 'Digitalgud' on the conference programme. A 2022 publication, 'From Community Involvement to Research Interests: Crowdsourcing Projects of the National Archives of Estonia' by Liisi Taimre, Aigi Rahi-Tamm, Sven Lepa and Tõnis Türna gives a sense of this history, including that in the late 19th / early 20th centuries, 'about 122,000 pages of Estonian folklore were collected by about 1,400 people from all over the country'. The paper references lots of Estonian crowdsourcing projects alongside participant survey data, so it's well worth a look.

The Finnish version is 'talkoot', which might be familiar as it's referenced in the name of an early, influential crowdsourcing project, 'DigitalKoot'. 'Barn raising' seems to be the most popular American term for it, and while volunteering is widespread in British and Australian culture, I can't think of a general term that describes the collective aspect of volunteering in communities.

Since those conversations I've wondered how a region's traditional volunteering culture intersects with crowdsourcing and citizen science, but never had time to research it. (But I did start collecting examples.) Is it easier to start and run projects in societies that already have offline models for cooperation and collective work? How do existing metaphors shape projects and contributions?

A recent post about the 'Nordic' term 'dugnadsånd', 'the collective willingness of people to come together in the context of community projects – emphasising cooperation and selflessness' prompted me to finally post about it. (The post also mentioned the Danish term 'arbejdsfællesskab' for a 'work community' and the Norwegian 'dugnad', glossed as 'voluntary work done together with other people', which might be fruitful for thinking about how language and metaphors for traditional collective work and volunteering shapes digital platforms. If you know of work in this area, I'd love to hear about it!)

But perhaps more importantly when so many regions face so many different challenges, it's a good reminder that working collectively for positive causes can support happier individuals and a more resilient society.

Notes from the Museum Data Service launch

I've shared these at work and thought it might be helpful to post my notes from the launch of the Museum Data Service at Bloomberg last week in public too.

The MDS aggregates museum (and museum-like) metadata, encouraging use by data scientists, researchers, the public, etc. The MDS doesn't include images, but links to them if they're available on museum websites. (When they open APIs, presumably people could build their own image-focused site on the service).

It was launched by Sir Chris Bryant (Minister of State at the Department for Science, Innovation and Technology and the Department for Culture, Media and Sport) who said it could be renamed the ‘Autolycus project', after Shakespeare's snapper up of unconsidered trifles. He presented it as a rare project that sits between his two portfolios.

Allan Sudlow of the AHRC (one of the funders) described it as secure, reliable digital infrastructure for GLAMs, especially providing security and sustainability for smaller museums, and meeting a range of needs, including reciprocal relationships between museums and researchers. He positioned it as part of the greater ecosystem, infrastructure for digital creativity and innovation. Kevin Gosling (Collections Trust) mentioned that it helps deliver the Mendoza Report's ‘dynamic collections'.

I'd seen a preview over the summer and was already impressed with the way it builds on decades of experience managing and aggregating real museum data between internal and centralised systems. They've thought hard about what museums really need to represent their collections, what they find hard about managing data/tech, and what the MDS can do to lighten that load.

The MDS can operate as a backup of last resort, including data that isn't shared even inside the organisation. They're not trying to pre-shape the data in any way, to allow for as many uses as possible (apart from the process of mapping specific museum data to their fields). It has persistent links (fundamental to FAIR data and citing records). They're linking to wikidata (and creating records there where necessary). APIs will be available soon (which might finally mean an end to the ‘does every museum need an API' debate).

The site https://museumdata.uk/ has records for institutions, collections, object records, and ‘new and enhanced data' about object records (e.g. exhibition interpretation, AI-generated keywords). It feels a bit like a rope bridge – lightweight but strong and flexible infrastructure that meets a community need.

Their announcement is at https://artuk.org/discover/stories/museum-data-service-will-revolutionise-access-to-the-uks-cultural-heritage

I admire the way they've used just enough technology to deliver it both practically and elegantly. They've also worked hard on explaining why it matters to different stakeholders, and finding models for funding and sustainability.

On a personal note, the launch was a bit like a school reunion (if you went to a particularly nerdy school). It was great to see people like David Dawson, Richard Light and Gordon McKenna there (plus Andy Ellis, Ross Parry and Kevin Gosling) as they'd shared visions for a service like this many years ago, and finally got to see it go live.

Links for a talk on crowdsourcing at UCL

I'm giving a lecture on 'crowdsourcing at the British Library' for students on UCL's MSc Sustainable Heritage taking the course 'Crowd-Sourced and Citizen Data for Cultural Heritage' (BENV0114).

As some links at the British Library are still down, I've put the web archive versions into a post, along with other links included in my talk:

Crowdsourcing at the British Library https://web.archive.org/web/20230401000000*/https://www.bl.uk/projects/crowdsourcing-at-the-british-library

The Collective Wisdom Handbook: perspectives on crowdsourcing in cultural heritage https://britishlibrary.pubpub.org/

The Collective Wisdom project website https://collectivewisdomproject.org.uk/

The Collective Wisdom 'Recommendations, Challenges and Opportunities for the Future of Crowdsourcing in Cultural Heritage: a White Paper' https://collectivewisdomproject.org.uk/new-release-collective-wisdom-white-paper/ (commentable version: https://docs.google.com/document/d/1HNEshEmS01CIdM31vX68Tg90-CNVSY0R2zbd7EUaCIA/edit?usp=sharing)

Living with Machines on Zooniverse http://bit.ly/LivingWithMachines. Some background: https://livingwithmachines.ac.uk/why-is-the-communities-lab-asking-people-to-read-old-news/

https://bl.uk/digital https://web.archive.org/web/20230601060241/https://www.bl.uk/subjects/digital-scholarship

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
https://labs.loc.gov/work/experiments/humans-loop/

Understanding visitor comments at scale – sentiment analysis of TripAdvisor reviews
https://medium.com/@CuriousThirst/on-artificial-intelligence-museums-and-feelings-598b7ba8beb6 

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 https://teachablemachine.withgoogle.com
  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'?

Fantastic Futures 2023 – AI4LAM in Vancouver

Reflections and selected highlights from the Fantastic Futures 2023 conference, held at the Internet Archive Canada's building in Vancouver and Simon Fraser University; full programme; videos will be coming soon.

A TL;DR is that it's incredible how many of the projects discussed wouldn't have been possible (or less feasible) a year ago. Whisper and ChatGPT (4, even more than 3.5) and many other new tools really have brought AI (machine learning) within reach. Also, the fact that I can *copy and paste text from a photo* is still astonishing. Some fantastic parts of the future are already here.

Other thinking aloud / reflections on themes from the event: the gap between experimentation and operationalisation for AI in GLAMs is still huge. Some folk are desperate to move onto operationalisation, others are enjoying the exploration phase – thinking about it, knowing where you and your organisation each stand on that could save a lot of frustration! Bridging it is possible, but it takes dedicated resources (including quality checking) from multi-disciplinary teams, and probably the goal has to be big and important enough to motivate all the work required. In examples discussed at FF2023, the scale of the backlog of collection items to be processed is that big important thing that motivates work with AI.

It didn't come up as directly, perhaps because many projects are still pilots rather than in production, but I'm very interested in the practical issues around including 'enriched' data from AI (or crowdsourcing) in GLAM collections management / cataloguing systems. We need records that can be enriched with transcriptions, keywords and other data iteratively over time, and that can record and display the provenance of that data – but can your collections systems do that?

Making LLMs stick to content in the item is hard, 'hallucinations' and loose interpretations of instructions are an issue. It's so useful hearing about things that didn't work or were hard to get right – common errors in different types of tools, etc. But who'd have thought that working with collections metadata would involve telling bedtime stories to convince LLMs to roleplay as an expert cataloguer?

Workflows are vital! So many projects have been assemblages of different machine learning / AI tools with some manual checking or correction.

A general theme in talks and chats was the temptation to lower 'quality' to be able to start to use ML/AI systems in production. People are keen to generate metadata with the imperfect tools we have now, but that runs into issues of trust for institutions expected to publish only gold standard, expert-created records. We need new conventions for displaying 'data in progress' alongside expert human records, and flexible workflows that allow for 'humans in the loop' to correct errors and biases.

If we are in an 'always already transitional' world where the work of migrating from one cataloguing standard or collections management tool to another is barely complete before it's time to move to the next format/platform, then investing in machine learning/AI tools that can reliably manage the process is worth it.

'Data ages like wine, software like fish' – but it used to take a few years for software to age, whereas now tools are outdated within a few months – how does this change how we think about 'infrastructure'? Looking ahead, people might want to re-run processes as tools improve (or break) over time, so they should be modular. Keep (and version) the data, don't expect the tool to be around forever.

Update to add: Francesco Ramigni posted on ACMI at Fantastic Futures 2023, and Emmanuelle Bermès posted Toujours plus de futurs fantastiques ! (édition 2023).

FF2023 workshops

I ran a workshop and went to two others the day before the conference proper began. I've put photos from the workshop I ran with Thomas Padilla (originally proposed with Nora McGregor and Silvia Gutiérrez De la Torre too) on Co-Creating an AI Responsive Information Literacy Curriculum workshop on Flickr. You can check out our workshop prompts and links to the 'AI literacy' curricula devised by participants.

Fantastic Futures Day 1

Thomas Mboa opens with a thought-provoking keynote. Is AI in GLAMs a Pharmakon (a purification ritual in ancient Greece where criminals were expelled)? Phamakon can mean both medicine and poison.

And discusses AI as technocoloniality e.g.Libraries in the age of technocoloniality: Epistemic alienation in African scholarly communications

Mboa asks / challenges the GLAM community:

  • Can we ensure cultural integrity alone, from our ivory tower?
  • How can we involve data-providers communities without exploiting them?
  • Al feeds on data, which in turn conveys biases. How can we ensure the quality of data?

Cultural integrity is a measure of the wholeness or intactness of material, whether it respects and honours traditional ownership, traditions and knowledge

Mboa on AI for Fair Work – avoiding digital extractivism; the need for data justice e.g. https://www.gpai.ai/projects/future-of-work/AI-for-fair-work-report2022.pdf.

Thomas Mboa finishes with 'Some Key actions to ensure responsible use of Al in GLAM':

  1. Develop Ethical Guidelines and Policies
  2. Address Bias and Ensure Inclusivity
  3. Enhance Privacy and Data Security
  4. Balance Al with Human Expertise
  5. Foster Digital Literacy and Skills Development
  6. Promote Sustainable and Eco-friendly Practices
  7. Encourage Collaboration and Community Engagement
  8. Monitor and Evaluate Al Impact:
  9. Intellectual Property and Copyright Considerations:
  10. Preserve Authenticity and Integrity]

I shared lessons for libraries and AI from Living with Machines then there was a shared presentation on Responsible AI and governance – transparency/notice and clear explanations; risk management; ethics/discrimination, data protection and security.

Mike Trizna (and Rebecca Dikow) on the Smithsonian's AI values statement. Why We Need an Al Values Statement – everyone at the Smithsonian involved in data collection, creation, dissemination, and/or analysis is a stakeholder – Our goal is to aspirationally and proactively strive toward shared best practices across a distributed institution. All staff should feel like their expertise matters in decisions about technology.

Jill Reilly mentioned 'archivists in the loop' and 'citizen archivists in the loop' at NARA, and Inventory of NARA Artificial Intelligence (AI) Use Cases

From Bart Murphy (and Mary Sauer Games)'s talk it seems OCLC are really doing a good job operationalising AI to deduplicate catalogue entries at scale, maintaining quality and managing cost of cloud compute; also keeping ethics in mind.

Next, William Weaver on 'Navigating AI Advancements with VoucherVision and the Specimen Label Transcription Project' – using OCR to extract text from digitised herbarium sheets (vouchers) and machine learning to parse messy OCR. More solid work on quality control! Their biggest challenge is 'hallucinations' and also LLM imprecision in following their granular rules. More on this at The Future of Natural History Transcription: Navigating AI advancements with VoucherVision and the Specimen Label Transcription Project (SLTP).

Next, Abigail Potter and Laurie Allen, Introducing the LC Labs Artificial Intelligence Planning Framework. I love that LC Labs do the hard work of documenting and sharing the material they've produced to make experimentation, innovation and implementation of AI and new technologies possible in a very large library that's also a federal body.

Abby talked about their experiments with generating catalogue data from ebooks, co-led with their cataloguing department.

A panel discussed questions like: how do you think about "right sizing" your Al activities given your organizational capacity and constraints? How do you think about balancing R&D / experimentation with applying Al to production services / operations? How can we best work with the commercial sector? With researchers? What do you think the role of LAMs should be within the Al sector and society? How can we leverage each other as cultural heritage institutions?

I liked Stu Snydman's description of organising at Harvard to address AI with their values: embrace diverse perspectives, champion access, aim for the extraordinary, seek collaboration, lead with curiosity. And Ingrid Mason's description of NFSA's question about their 'social licence' (to experiment with AI) as an 'anchoring moment'. And there are so many reading groups!

Some of the final talks brought home how much more viable ChatGPT 4 has made some tasks, and included the first of two projects trying to work around the fact that people don't provide good metadata when depositing things in research archives.

Fantastic Futures Day 2

Day 2 begins with Mike Ridley on 'The Explainability Imperative' (for AI; XAI). We need trust and accountability because machine learning is consequential. It has an impact on our lives. Why isn't explainability the default?

His explainability priorities for LAM: HCXAI; Policy and regulation; Algorithmic literacy; Critical making.

Mike quotes 'Not everything that is important lies inside the black box of AI. Critical insights can lie outside it. Why? Because that's where the human are.' Ehsan and Riedl.

Mike – explanations should be actionable and contestable. They should enable reflection, not just acquiescence.

Algorithm literacy for LAMs – embed into information literacy programmes. Use algorithms with awareness. Create algorithms with integrity.

A man presenting in a fancy old building. On a slide above, a quote and photo of a woman: "Technology designers are the new policymakers; we didn't elect them but their decisions determine the rules we live by." Latanya Sweeney Harvard University Director of the Public Interest Tech Lab

Policy and regulation for GLAMs – engage with policy and regulatory activities; insist on explainability as a core principle; promote an explanatory systems approach; champion the needs of the non-expert, lay person.

Critical making for GLAMs – build our own tools and systems; operationalise the principles of HCXAI; explore and interrogate for bias, misinformation and deception; optimise for social justice and equity

Mike quotes: "Technology designers are the new policymakers; we didn't elect them but their decisions determine the rules we live by." Latanya Sweeney (Harvard University, Director of the Public Interest Tech Lab)

Next: shorter talks on 'AI and collections management'. Jon Dunn and Emily Lynema shared work on AMP, an audiovisual metadata platform, built on https://usegalaxy.org/ for workflow management. (Someone mentioned https://airflow.apache.org/ yesterday – I'd love to know more about GLAMs experiences with these workflow tools for machine learning / AI)

Nice 'AI explorer' from Harvard Art Museums https://ai.harvardartmuseums.org/search/elephant presented by Jeff Steward. It's a really nice way of seeing art through the eyes of different image tagging / labelling services like Imagga, Amazon, Clarifai, Microsoft.

(An example I found: https://ai.harvardartmuseums.org/object/228608. Showing predicted tags like this is a good step towards AI literacy, and might provide an interesting basis for AI explainability as discussed earlier.)

Screenshot of a webpage with a painting of apples and the tags that different machine learning services predicted for the painting

Scott Young and Jason Clark (Montana State University) shared work on Responsible AI at Montana State University. And a nice quote from Kate Zwaard, 'Through the slow and careful adoption of tech, the library can be a leader'. They're doing 'irresponsible AI scenarios' – a bit like a project pre-mortem with a specific scenario e.g. lack of resources.

Emmanuel A. Oduagwu from the Department of Library & Information Science, Federal Polytechnic, Nigeria, calls for realistic and sustainable collaborations between developing countries – library professionals need technical skills to integrate AI tools into library service delivery; they can't work in isolation from ICT. How can other nations help?

Generative AI at JSTOR FAQ https://www.jstor.org/generative-ai-faq from Bryan Ryder / Beth LaPensee's talk. Their guiding principles (approximately):

  • Empowering researchers: focus on enhancing researchers' capabilities, not replacing their work
  • User-centred approach – technology that adapts to individuals, not the other way around
  • Trusted and reliable – maintain JSTOR's reputation for accurate, trustworthy information; build safeguards
  • Collaborative development – openly and transparently with the research community; value feedback
  • Continuous learning – iterate based on evidence and user input; continually refine

Michael Flierl shared a bibliography for Explainable AI (XAI).

Finally, Leo Lo and Cynthia Hudson Vitale presented draft guiding principles from the US Association of Research Libraries (ARA). Points include the need to include human review; prioritise the safety and privacy of employees and users; prioritise inclusivity; democratise access to AI and be environmentally responsible.

Finding Digital Heritage / GLAM tech / Digital Humanities jobs / staff

Technology job listings for cultural heritage or the humanities aren't always easy to find. I've recently been helping recruit into various tech roles at the British Library while also answering questions from folk looking for work in the digital heritage / GLAM (galleries, libraries, archives, museums) tech / digital humanities (DH)-ish world, so I've collated some notes on where to look for job ads. Plus, some bonus thoughts on preparing for a job search and applying for jobs.

Preparing for a job search / post

If you're looking for work, setting up alerts or subscribing to various job sites can give you a sense of what's out there, and the skills and language you'd want to include in your CV or portfolio. If you're going to advertise vacancies, it helps to get a sense of how others describe their jobs.

A photo of people in casual dress, lots of laptops, looking at a presentation 'Open Hack 2009 London'
Open Hack London, back in the day

Lurking on slacks and mailing lists gives you exposure to local jargon. Even better, if you can post occasionally to help someone with a question, as people might recognise your name later. Events – meetups, conferences, seminars, etc – can be good for meeting people and learning more about a sector. Serendipitous casual chats are easier in-person, but online events are more accessible.

Applying for GLAM/DH jobs

You probably know this, but sometimes a reminder helps… it's often worth applying for a job where you have most, but not all of the required skills. Job profiles are often wish lists rather than complete specs. That said, pay attention to the language used around different 'essential' vs 'desirable' requirements as that can save you some time. 

Please, please pay attention to the questions asked during the application process and figure out (or ask) how they're shortlisting based on the questions they ask. At the BL we can only shortlist with information that applicants provide in response to questions on the application. In other places, reflecting the language and specific requirements in the job ad and profile in your cover letter matters more. And I'm sorry if you've spent ages on it, but never assume that people can see your CV during the shortlisting or interview process.

If you see a technology or method that you haven't tried, getting familiar with it before an interview can take you a long way. Download and try it, watch videos, whatever – showing willing and being able to relate it to your stronger skills helps.

Speaking of interviews, these interview tips might help, particularly preparing potential answers using the STAR (situation, task, action, result) method.

The UK GLAM sector tends not to be able to offer visa sponsorship, but remote contracts may be possible. Always read the fine print…

Translate job descriptions and profiles to help candidates understand your vacancy

Updating to add: public organisations often have obscure job titles and descriptions. You can help translate jargon and public / charity / arts / academic sector speak into something closer to the language potential candidates might understand by writing blog posts and social media / discussion list messages that explain what the job actually involves, why it exists, and what a typical day or week might look like.

Cultural Heritage/Digital Humanities Slacks – most of these have jobs channels

GLAM/DH Mailing lists

Job sites with GLAM/DH vacancies

Toddlers to teenagers: AI and libraries in 2023

A copy of my April 2023 position paper for the Collections as Data: State of the field and future directions summit held at the Internet Archive in Vancouver in April 2023. The full set of statements is available on Zenodo at Position Statements -> Collections as Data: State of the field and future directions. It'll be interesting to see how this post ages. I have a new favourite metaphor since I wrote this – the 'brilliant, hard-working — and occasionally hungover — [medical] intern'.

A light brown historical building with columns and steps. The building is small but grand. A modern skyscraper looms in the background.
The Internet Archive building in Vancouver

My favourite analogy for AI / machine learning-based tools[1] is that they’re like working with a child. They can spin a great story, but you wouldn’t bet your job on it being accurate. They can do tasks like sorting and labelling images, but as they absorb models of the world from the adults around them you’d want to check that they haven’t mistakenly learnt things like ‘nurses are women and doctors are men’.

Libraries and other GLAMs have been working with machine learning-based tools for a number of years, cumulatively gathering evidence for what works, what doesn’t, and what it might mean for our work. AI can scale up tasks like transcription, translation, classification, entity recognition and summarisation quickly – but it shouldn’t be used without supervision if the answer to the question ‘does it matter if the output is true?’ is ‘yes’.[2] Training a model and checking the results of an external model both require resources and expertise that may be scarce in GLAMs.

But the thing about toddlers is that they’re cute and fun to play with. By the start of 2023, ‘generative AI’ tools like the text-to-image tool DALL·E 2 and large language models (LLMs) like ChatGPT captured the public imagination. You’ve probably heard examples of people using LLMs as everything from an oracle (‘give me arguments for and against remodelling our kitchen’) to a tutor (‘explain this concept to me’) to a creative spark for getting started with writing code or a piece of text. If you don’t have an AI strategy already, you’re going to need one soon.

The other thing about toddlers is that they grow up fast. GLAMs have an opportunity to help influence the types of teenagers then adults they become – but we need to be proactive if we want AI that produces trustworthy results and doesn’t create further biases. Improving AI literacy within the GLAM sector is an important part of being able to make good choices about the technologies we give our money and attention to. (The same is also true for our societies as a whole, of course).

Since the 2017 summit, I’ve found myself thinking about ‘collections as data’ in two ways.[3] One is the digitised collections records (from metadata through to full page or object scans) that we share with researchers interested in studying particular topics, formats or methods; the other is the data that GLAMs themselves could generate about their collections to make them more discoverable and better connected to other collections. The development of specialist methods within computer vision and natural language processing has promise for both sorts of ‘collections as data’,[4] but we still have much to learn about the logistical, legal, cultural and training challenges in aligning the needs of researchers and GLAMs.

The buzz around AI and the hunger for more material to feed into models has introduced a third – collections as training data. Libraries hold vast repositories of historical and contemporary collections that reflect both the best thinking and the worst biases of the society that produced them. What is their role in responsibly and ethically stewarding those collections into training data (or not)?

As we learn more about the different ‘modes of interaction’ with AI-based tools, from the ‘text-grounded’, ‘knowledge-seeking’ and ‘creative’,[5] and collect examples of researchers and institutions using tools like large language models to create structured data from text,[6] we’re better able to understand and advocate for the role that AI might play in library work. Through collaborations within the Living with Machines project, I’ve seen how we could combine crowdsourcing and machine learning to clear copyright for orphan works at scale; improve metadata and full text searches with word vectors that help people match keywords to concepts rather than literal strings; disambiguate historical place names and turn symbols on maps into computational information.

Our challenge now is to work together with the Silicon Valley companies that shape so much of what AI ‘knows’ about the world, with the communities and individuals that created the collections we care for, and with the wider GLAM sector to ensure that we get the best AI tools possible.

[1] I’m going to use ‘AI’ as a shorthand for ‘AI and machine learning’ throughout, as machine learning models are the most practical applications of AI-type technologies at present. I’m excluding ‘artificial general intelligence’ for now.

[2] Tiulkanov, “Is It Safe to Use ChatGPT for Your Task?”

[3] Much of this thinking is informed by the Living with Machines project, a mere twinkle in the eye during the first summit. Launched in late 2018, the project aims to devise new methods, tools and software in data science and artificial intelligence that can be applied to historical resources. A key goal for the Library was to understand and develop some solutions for the practical, intellectual, logistical and copyright challenges in collaborative research with digitised collections at scale. As the project draws to an end five and a half years later, I’ve been reflecting on lessons learnt from our work with AI, and on the dramatic improvements in machine learning tools and methods since the project began.

[4] See for example Living with Machines work with data science and digital humanities methods documented at https://livingwithmachines.ac.uk/achievements

[5] Goldberg, “Reinforcement Learning for Language Models.” April 2023. https://gist.github.com/yoavg/6bff0fecd65950898eba1bb321cfbd81.

[6] For example, tools like Annif https://annif.org, and the work of librarian/developers like Matt Miller and genealogists.

Little, “AI Genealogy Use Cases, How-to Guides.” 2023. https://aigenealogyinsights.com/ai-genealogy-use-cases-how-to-guides/

Miller, “Using GPT on Library Collections.” March 30, 2023. https://thisismattmiller.com/post/using-gpt-on-library-collections/.

Is 'clicks to curiosity triggered' a good metric for GLAM collections online?

The National Archives UK have a 'new way to explore the nation’s archives' and it's lovely: https://beta.nationalarchives.gov.uk/explore-the-collection/

It features highlights from their collections and 'stories behind our records'. The front page offers options to explore by topic (based on the types of records that TNA holds) and time period. It also has direct links to individual stories, with carefully selected images and preview text. Three clicks in and I was marvelling at a 1904 photo from a cotton mill, and connecting it to other knowledge.

When you click into a story about an individual record, there's a 'Why this record matters' heading, which reminds me of the Australian model for a simple explanation of the 'significance' of a collection item. Things get a bit more traditional 'catalogue record online' when you click through to the 'record details' but overall it's an effective path that helps you understand what's in their collections.

The simplicity of getting to an interesting items has made me wonder about a new UX metric for collections online – 'time to curiosity inspired', or more accurately 'clicks to curiosity triggered'. 'Clicks to specific item' is probably a more common metric for catalogue-based searches, but this is a different type of invitation to explore a collection via loosely themed stories.

'About' post https://blog.nationalarchives.gov.uk/new-way-to-explore-the-nations-archives/ and others under the 'Project ETNA' tag.

Screenshot of the Explore website, with colourful pictures next to headings like 'explore by topic', 'explore by time period' and 'registered design for an expanding travelling basket'

Live-blog from MCG's Museums+Tech 2022

The Museums Computer Group's annual conference has been an annual highlight for some years now, and in 2022 I donned my mask and went to their in-person event. And only a few months later I'm posting this lightly edited version of my Mastodon posts from the day of the event in November 2022… Notes in brackets are generally from the original toots/posts.

This was the first event that I live-blogged on Mastodon rather than live-tweeting. I definitely missed the to-and-fro of conversation around a hashtag, as in mid-November Mastodon was a lot quieter than it is even a few weeks later. Anyway, on with the post!

I'm at the Museums Computer Group's #MuseTech2022 conference.

Here's the programme https://museumscomputergroup.org.uk/events/museumstech-2022-turning-it-off-and-on-again/

Huuuuuuge thanks to the volunteers who worked so hard on the event – and as Chair Dafydd James says, who've put extra work into making this a hybrid event https://museumscomputergroup.org.uk/about/committee/

Keynote Kati Price on the last two and a half years – a big group hug or primal scream might help!

She's looking at the consequences of the pandemic and lockdowns in terms of: collaboration, content, cash, churn

Widespread adoption of tools as people found new ways of collaborating from home

Content – the 'hosepipe of requests' for digital content is all too familiar. Lockdown reduced things to one unifying goal – to engage audiences online

(In hindsight, that moment of 'we must find / provide entertainment online' was odd – the world was already full of books, tv, podcasts, videos etc – did we want things we could do together that were a bit like things we'd do IRL?)

V&A moved to capture their Kimono exhibition to share online just before closing for lockdown. Got a Time Out 'Time In'. No fancy tech, just good storytelling

Took a data-informed approach to creating content e.g. ASMR videos. Shows the benefits of 'format thinking'. Recommends https://podcasts.apple.com/us/podcast/episode-016-matt-locke/id1498470334?i=1000500799064 #MuseTech2022

V&A found that people either wanted very short or long form content; some wanted informative, others light-hearted content

Cash – how do you keep creating great experiences when income drops? No visitors, no income.

Churn – 'the great resignation' – we've seen a brain drain in the #MuseTech / GLAM sector, especially as it's hard to attract people given salaries. Not only in tech – loss of expert collections, research staff who help inform online content

UK's heading into recession, so more cuts are probably coming. What should a digital team look like in this new era?

Also, we're all burnt out. (Holler!) Emotional reserves are at an all-time low.

(Thinking about the silos – I feel my work-social circles are dwindling as I don't run into people around the building now most people are WFH most of the time)

Back from the break at #MuseTech2022 for more #MuseTech goodness, starting with Seb Chan and Indigo Holcombe-James on ACMI's CEO Digital Mentoring Program – could you pair different kinds of organisations and increase the digital literacy of senior leaders?

Working with a mentor had tangible and intangible benefits (in addition to making time for learning and reflection). The next phase was shorter, with fewer people. (Context for non-Australians – Melbourne's lockdown was *very* long and very restrictive)

(I wonder what a 'minimum viable mentorship' model might be – does a long coffee with someone count? I've certainly had my brain picked that way by senior leaders interested in digital participation and strategy)

Lessons – cross-art form conversations work really well; everyone is facing similar challenges

(Side note – I'm liking that longer posts mean I'm not dashing off posts to keep up with the talks)

Next up #MuseTech2022 Stephanie Bertrand https://twitter.com/sbrtrandcurator on prestige and aesthetic judgement in the art world. Can you recruit the public's collective intelligence to discover artworks? But can you remove the influence of official 'art world' taste makers in judging artworks?

'Social feedback is a catch-22' – can have runaway inequality where popular content becomes more popular, and artificial manipulation that skews what's valued?

Now Somaya Langley https://twitter.com/criticalsenses on making digital preservation an everyday thing. (Shoutout to the awesome #DigiPres folk who do this hard work) – how can a whole organisation include digital preservation in its wider thinking about collections and corporate records? What about collecting born-digital content so prevalent in modern life?

(Side note – Australia seems to have a much stronger record management culture within GLAMs than in the UK, where IME you really have to search to find organisational expectations about archiving project records)

#MuseTech2022 Somaya's lessons learnt include: use the three-legged stool of digital preservation of technology, resources and organisation https://deepblue.lib.umich.edu/bitstream/handle/2027.42/60441/McGovern-Digital_Decade.html?sequence=4 – approach it holistically

Help colleagues learn by doing

Moving from Projects to Programmes to Business as Usual is hard

Help people be comfortable with there not being one right answer, and ok with 'it depends'

#MuseTech2022 Next up in Session 2: Collections; Craig Middleton, Caroline Wilson-Barnao, Lisa Enright – documenting intense bushfires in Aus summer 2019/20 and COVID. They used Facebook as a short-term response to the crisis; planned a physical exhibition but a website came to seem more appropriate as COVID went on. https://momentous.nma.gov.au has over 300 unique responses. FB helpful for seeing if a collecting idea works while it's timely, but other platforms better for sustained engagement. Also need to think about comfort levels about sharing content changing as time goes on.

Museums can be places to have difficult conversations, to help people make sense of crises. But museums also need to think beyond physical spaces and include digital from the start.

Also hard when museum people are going through the same crises (links back to Kati's keynote about what we lived through as a sector working for our audiences while living through the pando ourselves)

#MuseTech2022 David Weinczok 'using digital media to go local'

60% of National Museums Scotland's online audiences have never visited their museums. 'Telling the story of an object without the context of the landscape and community it came from' can help link online and in-person audiences and experiences

'Museum Screen Time' – experts react to pop culture depictions of their subject area eg Viking culture https://www.nms.ac.uk/explore-our-collections/films/museum-screen-time-viking-age/

Blog series 'Objects in Place' – found items in collections from a particular area, looked to tell stories with objects as 'connective threads', not the focus in themselves

'What can we do online to make connections with people and communities offline?'

(So many speakers are finishing with questions – I love this! Way to make the most of being in conversation with the musetech community here)

Next at #MuseTech2022, Amy Adams & Karen Clarke, National Museum of the Royal Navy – digital was always lower priority before COVID; managed to do lots of work on collections data during lockdowns.

They finally got a digital asset management (DAM) system, but then had to think about maintaining it; explaining why implementation takes time. Then there was an expectation that they could 'flip a switch' and put all the collections online. Finding ways to have positive conversations with folk who are still learning about the #MuseTech field.

Also doing work on 'addressing empires' – I like that framing for a very British institution.

Now Rebecca Odell, Niti Acharya, Hackney Museum on surviving a cyber attack. Lost access to collections management database (CMS) and images. Like their digital building had burnt down. Stakeholder and public expectations did not adjust accordingly! 14 months without a CMS.

Know where your backups are! Export DBs as CSV, store it externally. LOCKSS, hard drives

#MuseTech2022 Rebecca Odell, Niti Acharya, Hackney Museum continued – reconstructing your digital stuff from backups, exports, etc takes tiiiiiiime and lots of manual work. The sector needs guides, checklists, templates to help orgs prepare for cyber attacks.

(Lots of her advice also applies to your own personal digital media, of course. Back up your backups and put them lots of places. Leave a hard drive at work, swap one with a friend!)

New Q&A game – track the echo between remote speakers and the AV system in the back. Who's unmuted that should be muted? [One of the joys of a hybrid conference]

We'll be heading out to lunch soon, including the MCG annual general meeting

#MuseTech2022

(Missed a few talks post-lunch)

Adam Coulson (National Museums Scotland) on QR codes:
* weren't scanned in all exhibition/gallery contexts
* use them to add extra layers, not core content
* don't assume everyone will scan
* discourage FOMO (explain what's there)
* consider precious battery life

More at https://blog.nms.ac.uk/2022/07/19/qr-codes-in-museums-worth-the-effort/

Now Sian Shaw (Westminster Abbey) on no longer printing 12,000 sheets of paper a week (given out to visitors with that day's info). Made each order of service (dunno, church stuff, I am a heathen) at the same URL with templates to drop in commonly used content like hymns

It's a web page, not an app – more flexible, better affordances re your place on the page

Some loved the move to sustainability but others don't like having phones out in church.

Ultimately, be led by the problem you're trying to solve (and there's always a paper backup for no/dead phone folk)

Q&A discussion – take small steps, build on lessons learnt

#MuseTech2022 Onto the final panel, 'Funding digital – what two years worth of data tells us'

(It's funny when you have an insight into your own #MuseTech2022
life via a remark at a conference – the first ever museum team I worked in was 'Outreach' at Melbourne Museum, which combined my digital team with the learning team under the one director. I've always known that working in Outreach shaped my world view, but did sitting next to the learning team also shape it?)

And now Daf James is finishing with thanks for the committee members behind the MCG generally and the event in particular – big up @irny for keeping the tech going in difficult circumstances!

Daf James welcomes online and in-person attendees to the Museums Computer Group's Museums+Tech 2022 conference