AI & SOCIETY - Special Issue on When data turns into archives: making digital records more accessible with AI
GUEST EDITOR:
Dr Lise Jaillant, Reader (Associate Professor) in Digital Humanities, Loughborough University, UK, l.jaillant [at] lboro.ac.uk
SYNOPSIS:
Over the years, Artificial Intelligence has become a ubiquitous technology for everyday use. One area that has received comparatively less attention is the use of AI to discover, organize, and filter the copious amounts of data generated by government professionals. With the digital turn, the vast majority of government data is now created in digital form. Emails have replaced letters, PDFs and Word documents have replaced paper memos, and audio/visual files are stored on local hard drives and in various systems. Record creators must transfer data to archival institutions for long term preservation and access.
Much public good could be derived from the analysis of government records. Yet, access to these data is extremely difficult. Archival emails, audio/video files and other born-digital records are rarely accessible to users for many reasons - including confidentiality, privacy, national security, copyright, technological constraints and a lack of organization. In addition, many government records are created outside official channels (for example using WhatsApp or personal email accounts). The rise of this “shadow IT” creates its own issues in terms of accountability, discoverability and accessibility of this data.
The problem of locked digital data is a complex challenge that requires collaboration across multiple fields and professional sectors. AI can be used to identify sensitive materials in a mass of born-digital records to make non-sensitive materials accessible. AI can also automatically create metadata, when the original metadata is missing. In addition, AI can serve to search vast amounts of data, when keyword searches would not be effective. New technologies have the potential to unlock data and expand the proportion of records sent to archival repositories.
The issue of inaccessible archival records is not limited to data created by the government. For the cultural heritage sector, giving access to born-digital records that may contain personal and sensitive information is a major challenge. This has an impact on historians and other scholars who need access to archival data for their research. It also impacts other users (such as journalists and third sector professionals) who rely on data for public information and advocacy.
The overall aim of this special issue is to explore how AI can help improve the preservation, access and usability of digital and born-digital archives. It focuses on the perspective and the challenges that AI can offer in unlocking archival data in the government sector and beyond.
The articles will offer new interdisciplinary theoretical interpretations, apply research methodologies to new case studies, and offer new perspectives on the present and future perspective of AI applied to data and digital archives.
We encourage submissions that address these research questions using a range of theoretical frameworks (including critical data studies, digital humanities, archival studies, cultural heritage studies) and research methods (both qualitative and quantitative – including data and computer science methods). Contributions from practitioners in government and the Libraries, Archives and Museums sector are welcome, as are contributions from professionals and academics at an early career stage.
The proposed special issue is a key research output of the LUSTRE project funded by the Arts and Humanities Research Council (AHRC) in the UK. The overall aim of the LUSTRE project is to connect policy makers with computer scientists, digital humanists and professionals in the GLAM sector (Galleries, Libraries, Archives and Museums). More information on the project can be found on our website: https://lustre-network.net/ (this opens in a new tab)
SPECIAL ISSUE THEMES:
Bringing together digital humanists and social scientists, AI experts, professionals in Information Management, archivists, librarians, and museum professionals, this special issue welcomes contributions that explore themes including, but not limited to:
- AI applied to archival data created by government, cultural heritage organizations or other institutions
- “Digital Heap” and the issue of disorganized data
- Making archival data more accessible for public good
- Risks associated with AI applied to born-digital records
- Mitigating these risks: AI and ethics /Designing responsible AI systems
- Research methods (including AI approaches) to use archival data
- Qualitative approaches, for example to survey professional attitudes towards AI and archives.
IMPORTANT DATES:
Abstract submission: 30th June 2024
Manuscript submission: 30th September 2024
Review and editorial window: October - December 2024
Revised paper due: 31st January 2025
SUBMISSION FORMATTING:
You can find more information about formatting under the section “Submission guidelines” https://www.springer.com/journal/146 (this opens in a new tab). For inquiries and to submit your abstract (300 words) by email, please contact l.jaillant[at]lboro.ac.uk