Artificial Intelligence and the assessment of data sensitivity in cultural organisations: an exploration of the possibilities and implications in the context of a memory institution

With support from AHRC/NPIF though the Scottish Graduate School for Arts and Humanities, Information Studies at the University of Glasgow, in collaboration with the National Library of Scotland, seeks a highly motivated graduate to undertake Ph.D. research, to investigate the effectiveness and appropriateness of using artificial intelligence (AI) for reviewing data for diverse forms of sensitivity at the National Library of Scotland. The research will use innovative methods for handling sensitive information, focusing on compliance with legal obligations (e.g. data protection). It will also investigate broader concerns, such as cost and data ethics of incorporating AI in data handling, recommending guidelines for using AI as a component for scalable policies regarding sensitive data at the Library.

Recent developments in data protection law (e.g. the General Data Protection Regulation, GDPR) require renewed action from all sectors (academic, industry, government and cultural organisations), including the National Library of Scotland, for the assessment of the means and methods of compliance. The rapid increase in data volumes over the last three decades, however, makes meeting legal obligations, while maintaining continued improvement of trusted services provided to citizens, a hard task to achieve, without the employment of scalable artificial intelligence techniques (e.g. information retrieval, natural language processing, machine learning). Hitherto computing science has been perceived as the venue for such artificial intelligence research. The research proposed here endeavours to broaden participation to the arts and humanities and cultural organisations such as National Library of Scotland.

The project aims to improve workflow productivity within memory institutions through the incorporation of AI (e.g. machine learning, natural language processing, information retrieval) in data and documentary content analysis. This will involve experimentation and a comprehensive exploration of artificial intelligence as a means of reviewing data and supporting policies regarding sensitive data. While some categories of sensitivity, such as in relation to personal data, may be understood through legal definitions, there can be considerable uncertainty when it comes to defining broader concepts of sensitive information, which can be subject to factors such as cultural and temporal climates. All forms of sensitive content, however, can be extremely challenging to identify or isolate.

The proposed project investigates AI responsive to such uncertainty, to complement human decisions about sensitivity. The research will explore four primary questions: (1)What is the viability of applying AI enabled learning technologies to the analysis of data and documents to adjudicate concerns about their public release; (2) What levels require reviewing (e.g. collection, objects, segments), how do these map to changing areas of policy (e.g. GDPR), and what type of material (e.g. images, text, audio, video) need accommodating? (3) What are the social concerns regarding AI as a means of carrying out personal/sensitive data review, and how can we evaluate their ability to address these concerns?, and (4) How feasible is adaptive human-AI collaboration for protocol development beyond identification of sensitive data? What are the costs, limitations and opportunities?

The research will first test existing approaches on the data/documents (possibly to include non-textual and/or Scots/Gaelic material) at the National Library of Scotland to assess their ability to address these broader definitions, with a wider scope of features. Later, approaches that allow the AI to learn features in changing contexts will be explored. The project will make direct recommendations to inform the Library’s policies, exploring applicability to other sectors. Assessing social implications of AI strengthens adaptability.

In the first instance, contact Yunhyong Kim ([Email Address Removed]) to discuss the project informally and to discuss eligibility.

Send your funding application form, CV and 2 references to Yunhyong Kim ([Email Address Removed]) by 17:00 on 29 August 2018.
Interviews are likely to take place in the first week of September.

Successful scholarship applicants will be invited to apply for admission to the relevant PhD programme after they have been selected for funding. This studentship has a strict start date of 1 October 2018.