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Citizen-Led Gen Ai Governance: Models, Tools and Practice

School of Electronics, Electrical Engineering and Computer Science | PHD

Applications are now CLOSED
Funding
Funded
Reference Number
EEECS/2025/MF1
Application Deadline
28 February 2025
Start Date
1 October 2025

Overview

This research seeks to investigate citizen-led approaches to AI governance, with specific focus on Generative AI. Emphasis is on the design and development of digital tools that can support a values-first [2] participatory decision-making process where agreed values and principles pertaining the use and production of Gen AI can be mapped to concrete human and software behaviour [3].

Traditional approaches to AI governance often rely on expert-led frameworks and top-down regulations, hence overlooking the diverse lived experiences of citizens who are impacted by these technologies (e.g. impact on labour [1]).

In addition, a general trend in both computing research is to turn to AI ethics [6], but this often fails to address the gap between abstract principles and practice [3], often alienating key stakeholders in the process (e.g. “Ethics fatigue” [5]).

In contrast, research on human values in software engineering (SE) has shown the importance of moving beyond abstract principles, and explored how to systematically capture how values are interpreted, prioritised, and enacted in SE decision making process [2].

Research also shows that citizens' assembly style approaches hold potentials to address complex societal challenges by bringing together diverse perspectives in a deliberative process [6].

However, existing digital platforms supporting deliberative processes face significant limitations in the context of decision-making around rapidly evolving technologies which require both technical knowledge and hands-on practice. For instance, tools focusing on democratic decision making [3] have not been designed for the unique challenges of Generative AI, which requires rapid and evolving ‘skilling-up’ to support informed decision-making [4].

Building on successful elements of existing digital democracy initiatives while addressing their current limitations, this research aims to develop novel approaches and tools to support deliberation and translate informed decisions into concrete guidance for software use and production.

Research Domain:

The specific domain of the proposal has been left deliberately open: use cases may focus on education, research, and the private, public and third sector; candidates are advised to specify their preferences in their application. This project can be taken by more than one research student.

Candidate description:
The candidate must have at a demonstrable interest in human-centric aspects of computing (e.g., within Software Engineering and Human Computer Interactions) including participatory approaches to systems design and development.

References
[1] Cazzaniga, M. et al. 2024. Gen-AI: Artificial Intelligence and the Future of Work. IMF Staff Discussion Note SDN2024/001, International Monetary Fund, Washington, DC.
[2] Ferrario, M.A., et al. 2016. Values-first SE: research principles in practice. In Proceedings of the 38th international conference on software engineering ICSE-SEIS (pp. 553-562).
[3] Ferrario, M. A., & Winter, E. (2023). Applying Human Values Theory to Software Engineering Practice: Lessons and Implications. IEEE Transactions on Software Engineering.
[4] Holston, J., Issarny, V. and Parra, C. 2016. Engineering software assemblies for participatory democracy: The participatory budgeting use case. In Proceedings of the 38th International Conference on Software Engineering Companion (pp. 573-582).
[5] Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: an initial review of publicly available AI ethics tools, methods and research to translate principles into practices. Science and Engineering Ethics, 26(4), 2141-2168.
[6] Khan, A.A., et al. 2022. Ethics of AI: A systematic literature review of principles and challenges. In Proceedings of the 26th International Conference on Evaluation and Assessment in Software Engineering (pp. 383-392).
[7] Willis, R., Curato, N. and Smith, G., 2022. Deliberative democracy and the climate crisis. Wiley Interdisciplinary Reviews: Climate Change, 13(2), p.e759.

Funding Information

To be eligible for consideration for a Home DfE or EPSRC Studentship (covering tuition fees and maintenance stipend of approx. £19,237 per annum), a candidate must satisfy all the eligibility criteria based on nationality, residency and academic qualifications.

To be classed as a Home student, candidates must meet the following criteria and the associated residency requirements:

• Be a UK National,
or • Have settled status,
or • Have pre-settled status,
or • Have indefinite leave to remain or enter the UK.

Candidates from ROI may also qualify for Home student funding.

Previous PhD study MAY make you ineligible to be considered for funding.

Please note that other terms and conditions also apply.

Please note that any available PhD studentships will be allocated on a competitive basis across a number of projects currently being advertised by the School.

A small number of international awards will be available for allocation across the School. An international award is not guaranteed to be available for this project, and competition across the School for these awards will be highly competitive.

Academic Requirements:

The minimum academic requirement for admission is normally an Upper Second Class Honours degree from a UK or ROI Higher Education provider in a relevant discipline, or an equivalent qualification acceptable to the University.

Project Summary
Supervisor

Dr Maria Angela Ferrario

m.ferrario@qub.ac.uk

Research Profile


Mode of Study

Full-time: 3 or 3.5 years


Funding Body
Funding TBC
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