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Operationalising AI at a national library



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Operationalising AI at a national library

  1. 1. Operationalising AI at a national library 1 Dr. Mia Ridge, @mia_out Digital Curator, British Library @BL_DigiSchol @LivingWMachines Museums + AI Network, New York, September 2019
  2. 2. The British Library is the national library of the UK. By law we receive a copy of every publication produced in the UK and Ireland. We have up to 200 million items, including: 14m books; 8m stamps; 310,000 manuscript volumes; 4m maps; websites; television and radio news About 5% is digitised or born- digital.
  3. 3. The British Library's Digital Scholarship team 3 Our mission is to enable the use of the British Library’s digital collections for research, inspiration, creativity, and enjoyment. Digital Research Team Endangered Archives Living with Machines BL Labs Connect and share Support digital scholars Agents for change Invest in our staff Innovate and collaborate
  4. 4. Enabling a shift from pages to datasets 4 ScalePerspectiveSpeed Complexity
  5. 5. Our Partners Our Funders Living with Machines Rethinking the impact of technology on the lives of ordinary people during the Industrial Revolution @LivingWMachines
  6. 6. Living with Machines aims to: • Generate new historical perspectives on the effects of the mechanisation of labour on the lives of ordinary people during the long nineteenth century. • Support the wider academic and cultural heritage sector in using digital methods to answer historical questions. • Create new tools and code that can be reused and built upon in future projects. • Develop new computational techniques for working with historical research questions. • Enrich the British Library’s data holdings for the benefit of all • Advance public awareness of how digital research in the humanities can enhance understanding of history.
  7. 7. We’re working on questions like… • How do we encourage ‘radical collaboration’ between disciplines and organisations? • How do we integrate crowdsourcing and machine learning across shared timelines? • How can we chart and understand representativeness, genre balance and bias in sources? • Were machines seen as autonomous agents in the 19thC, able to bring about change?
  8. 8. Sources include… • Full-text: newspapers, trade and postal directories, autobiographies, journals and diaries, novels, Parliamentary papers • Tabular: census records, BMD • Visual: Ordnance Survey maps, Goad fire insurance maps, images in serials/text as image • Mostly British Library collections but we’re negotiating access to other collections and derived data
  9. 9. Benefits for the British Library / GLAMs • Enhance Turing partnership through research collaboration • Enhance BL reputation as a leading digital innovator in the library sector • Improve working with large scale digitisation, digital content, and data • Better incorporate learnings and outcomes of research projects • Improve digital workflows and processes • Improve workflows for ingesting or enhancing metadata • Grow digital collections • Increase understanding of and ability to apply advanced methods • Increased awareness of data science and digital history • Provide a coherent model for mixed-rights access to items and datasets • Digital content and data in the cultural programme (exhibitions)
  10. 10. Challenges in operationalising AI: copyright • Legal exemptions for some forms of text mining require either internal infrastructure to support data science and AI at scale or negotiating rights issues for newspapers digitised by a third party • Resolving data protection (GDPR) questions • Having funds for digitisation is wonderful but it highlights the impact of our 'safe' 1878 date on scholarship • Technical and usability challenges for publishing (partial) datasets and derived datasets within a complex rights environment
  11. 11. Challenges in operationalising AI: scale • Data storage and processing at terabyte scale quickly becomes expensive • New workflows for digitised images • Supporting academics in selecting digitisation at scale • Encouraging and enabling the project to work with complexity at scale
  12. 12. Challenges in operationalising AI: operational • Managing impact on related teams who are being asked to answer new types of queries and provide different types of support, sometimes with more urgency than usual • How do we integrate AI-generated metadata at scale into strategic systems? • What impact does the provision of many millions of very detailed ‘entity recognition’ annotations have on discovery services? • What public-facing infrastructure can we create? Do all outputs (statistical models vs metadata) need to be equally sustainable?
  13. 13. Challenges in operationalising AI: interdisciplinary • Aligning GLAM and academic data science goals, outcomes, timelines and reward structures? • Turning academic outputs (working papers, peer reviewed publications, software models) into training materials (tutorials with related datasets, workshops, blog posts) for BL staff, library users and other scholars • Turning research findings and methods into a small exhibition • Integrating participation through crowdsourcing and work in local libraries with academic research processes
  14. 14. 14 Data science in the library? ‘you need the right team and the right mindset. The latter requires a cultural shift that prioritizes and rewards experimentation, measurement, and testing throughout your organization’ Google, ‘Everything a marketer needs to know about machine learning’
  15. 15. Our Partners Our Funders Thank you! Questions? Dr Mia Ridge, Digital Curator, British Library @mia_out @BL_DigiSchol @LivingWMachines

Editor's Notes

  • Aligning academic goals (publications) with library goals (public interfaces, datasets, tools)
  • We’d like lots of lightbulb moments but are we set up for them?
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