As AI systems produce cultural outputs (language, images, decisions) rather than merely numerical predictions, governance faces a fundamental challenge: regulatory frameworks designed for technical systems now confront technologies that operate in spaces of cultural meaning, ambiguity, and context. When AI makes decisions about healthcare, employment, or justice, questions of accountability, transparency, and fairness become matters of interpretation rather than calculation. Yet current governance approaches often treat these concepts as if they have singular, technical definitions, when in reality they are culturally situated and context-dependent.
Your research in this theme could explore how legal and regulatory frameworks must evolve beyond technical compliance to engage with the interpretive complexity of AI governance. Rather than asking only "how does this system work?" (technical explainability), you might investigate "what does this system mean?" in diverse cultural contexts. How can governance frameworks account for the reality that fairness, transparency, and accountability are understood differently across communities? What forms of regulation enhance rather than diminish human agency as we move beyond treating AI as mere assistants toward more complex human-AI relationships?
Potential directions include examining how current regulatory approaches risk entrenching homogenised conceptions of responsible AI that fail to reflect diverse human values and experiences, developing participatory governance mechanisms that redistribute interpretive authority to affected communities, or exploring how accountability frameworks can be reimagined when AI systems operate across fragmented value chains and cultural contexts. You might study how the UK's principles-based approach and the EU's comprehensive regulation each encode particular assumptions about what AI governance should achieve, or investigate emerging challenges where generative and autonomous systems resist traditional regulatory categories.
This research requires truly transdisciplinary collaboration, integrating perspectives from human-computer interaction, legal scholarship, policy studies, humanities, and the lived experiences of communities whose cultural worlds AI systems increasingly shape.
Place-based and Regional Context
The North East of England offers unique opportunities for examining AI governance in practice. As one of the UK's most digitally connected regions, local authorities including Newcastle City Council and the North of Tyne Combined Authority are deploying AI systems in public services whilst grappling with transparency and accountability requirements. Regional police forces are implementing algorithmic tools for resource allocation and risk assessment, creating opportunities to study how contestability mechanisms work (or fail) for citizens.
The region's strong public sector presence, combined with areas of significant deprivation, means AI deployment decisions have immediate consequences for vulnerable communities. Your research could work directly with organisations like VONNE (the voluntary sector infrastructure body) to understand how AI systems affect citizens accessing services, or partner with regional health bodies to examine AI governance in healthcare contexts. The presence of DSIT officials and connections to national policymaking through partner organisations creates pathways for research to directly inform UK AI regulation as it evolves beyond the current principles-based approach towards potential legislation.
Relevant Partner Organisations
This theme connects with partners spanning policymaking, regulation, and public services. At the national level, relationships with DSIT, the Cabinet Office, and 10 Downing Street provide insight into how AI governance policy develops and could be improved. Regulatory partners including Ofcom and the National Cyber Security Centre offer perspectives on sector-specific implementation challenges.
Public sector partners such as Newcastle City Council, DWP, NHS organisations, Northumbria Police, and Cleveland Police deploy AI systems directly affecting citizens, creating research opportunities to examine governance frameworks in practice. These partnerships enable you to study not just policy design but implementation realities, working with organisations facing the daily challenges of making AI governance principles actionable within resource constraints and complex institutional contexts.
Related Articles and Reading
Participatory AI Governance and Power
Transparency, Explainability, and Understanding
Accountability, Contestability, and Fairness
Public Sector AI and Citizen Perspectives
UK Policy and Regulatory Framework
EU Policy and Regulatory Framework
Emerging Challenges: Generative and Agentic AI
Cross-cutting Resources
Back to top
