Artificial Intelligence (AI) and the assessment of data sensitivity in cultural organisations
Lead Research Organisation:
University of Glasgow
Department Name: College of Arts
Abstract
This project proposes timely research to investigate the effectiveness and appropriateness of using artificial intelligence (AI) methods for reviewing data for diverse forms of sensitivity at the National Library of Scotland, focusing on
compliance with legal obligations (e.g. GDPR), but also exploring broader themes of sensitive information. In addition to innovative methods for handling sensitive information, the research will 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 and beyond.
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.
The project will:
1. Evaluate and recommend AI approaches that are likely to be beneficial, not only by prototyping and/or testing AI performance (e.g. effectiveness and efficiency in identifying sensitive and/or personal information), but, by assessing their ability to address social concerns that have been identified.
2. Assess social concerns, for example, ethical and economic risks, costs, benefits, and opportunities associated with AI-driven methods of data review as part of determining or managing public access to documents.
3. Build capacity to go beyond identification of sensitive and/or personal information, to stimulate decision-making and intelligent organisational policy development.
4. Foster transfer learning, by developing AI approaches that use knowledge gained while solving one problem to a different but related problem.
5. Examine the boundaries between protection of individual rights, and particularly those of vulnerable people, and documentary censorship and the implications of using automation in setting these boundaries.
Research Question or Problem:
- What is the viability of applying AI enabled learning technologies, to the analysis of data and documents to adjudicate concerns about their public release.
- What levels require reviewing (collection, objects, segments), how do these map to emerging policies (e.g. GDPR), and what type of material (e.g. images, text, audio, video) need accommodating?
- 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?
- How feasible is adaptive human-AI collaboration for protocol development beyond identification of sensitive data? What are the costs, limitations and opportunities?
compliance with legal obligations (e.g. GDPR), but also exploring broader themes of sensitive information. In addition to innovative methods for handling sensitive information, the research will 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 and beyond.
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.
The project will:
1. Evaluate and recommend AI approaches that are likely to be beneficial, not only by prototyping and/or testing AI performance (e.g. effectiveness and efficiency in identifying sensitive and/or personal information), but, by assessing their ability to address social concerns that have been identified.
2. Assess social concerns, for example, ethical and economic risks, costs, benefits, and opportunities associated with AI-driven methods of data review as part of determining or managing public access to documents.
3. Build capacity to go beyond identification of sensitive and/or personal information, to stimulate decision-making and intelligent organisational policy development.
4. Foster transfer learning, by developing AI approaches that use knowledge gained while solving one problem to a different but related problem.
5. Examine the boundaries between protection of individual rights, and particularly those of vulnerable people, and documentary censorship and the implications of using automation in setting these boundaries.
Research Question or Problem:
- What is the viability of applying AI enabled learning technologies, to the analysis of data and documents to adjudicate concerns about their public release.
- What levels require reviewing (collection, objects, segments), how do these map to emerging policies (e.g. GDPR), and what type of material (e.g. images, text, audio, video) need accommodating?
- 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?
- How feasible is adaptive human-AI collaboration for protocol development beyond identification of sensitive data? What are the costs, limitations and opportunities?
Organisations
People |
ORCID iD |
| Yunhyong Kim (Primary Supervisor) | |
| Rebecca Oliva (Student) |