P30 Governing AI in Public Administration: Responsiveness, Expertise, and Capacity

Panel Chairs

Corresponding chair

• Prof. Lise Rykkja, Department of Government, University of Bergen.  Lise.Rykkja@uib.no
• Prof. dr. Madalina Busuioc (corresponding chair), Department of Political Science and Public Administration, Vrije Universiteit Amsterdam. Co-Corresponding Chair. e.m.busuioc@vu.nl

Track 1

• Prof. Lise Rykkja, Department of Government, University of Bergen. Corresponding chair. Lise.Rykkja@uib.no
• Dr. Jonas Lund-Tønnesen, Department of Government, University of Bergen. Co-Chair. 
• Dr. Charlotta Kronblad, Gothenburg University. Co-Chair. 

Track 2

• Prof. dr. Madalina Busuioc, Department of Political Science and Public Administration, Vrije Universiteit Amsterdam. Corresponding Chair. e.m.busuioc@vu.nl
• Dr. Johan Christensen, Institute of Public Administration, Leiden University. Co-Chair.
• Dr. Federica Fusi, Department of Political Science and Public Administration, Vrije Universiteit Amsterdam. Co-Chair. 

Description of the Panel Topic

This panel examines how the adoption of AI affects public administrations’ internal epistemic workings and outward-facing (citizen-state) relations. It investigates the impact of AI on government responsiveness and accountability to citizens on the one hand, and its impact on internal institutional knowledge, capacity and political-administrative relations on the other.

While AI promises greater efficiency and service delivery and enhanced information processing, it raises concerns about transparency, fairness and accountability. More fundamentally, AI may reshape how public organizations produce and deploy knowledge, exercise professional judgment, and distribute authority between human actors and technological systems.

Governing AI adoption in government responsibly raises key questions: how organizations can preserve professional knowledge and judgment while realizing the benefits of automation; how accountability can be maintained when decisions depend on opaque, probabilistic, or outsourced systems; and how citizens’ experiences, expectations, and rights can inform the design and governance of AI-enabled public services. The panel is structured along two main tracks:

Track 1

Track 1 focuses on the outward impact of AI on citizen-state relations and implications for bureaucratic and societal responsiveness.

The track examines how public organizations adopt technologies that are both responsible and responsive to citizen needs and organizational capacities and practices. Responsiveness is understood not simply as a feature of technical design, but as an institutional outcome shaped by governance arrangements, organizational practices, professional norms, administrative capacity, and citizen-state interactions.

Contributions in this track may explore how public organizations balance AI-enabled efficiency with discretion, and accountability, and how these trade-offs affect organizational capacity and citizen experiences. They may examine how AI-enabled monitoring expands surveillance and reshapes citizen–state relations. We welcome theoretical and empirical contributions drawing on public administration and management, including theories of bureaucratic responsiveness, reputation, governance capacity, surveillance, co-production, and digital governance.

Track 2

Track 2 focuses on the impact of AI on the internal workings of public administrations, with a focus on how AI reshapes knowledge production and political-bureaucratic relations.

By taking over expertise tasks traditionally performed by the bureaucracy, AI tools replace and offer an alternative to bureaucratic expertise and policy advice, fundamentally reshaping where knowledge is located, how it is produced, which knowledge its prioritized, as well as its very nature –shifting toward increasingly opaque, inscrutable, and probabilistic forms. Given the role of bureaucratic knowledge as the primary currency in political-administrative relations, these shifts stand to bring unprecedented disruption to established institutional dynamics.

This track seeks contributions addressing how AI affects the foundations of political-administrative systems and core theories of the bureaucracy; the epistemic impact of AI on professional knowledge and experts’ role, policy-making, and political-bureaucratic relationships; the reconfiguration of government logics, structures, and institutions in response to AI’s impact on knowledge; consequences for accountability and safeguards on public power; and the socio-technical implications of AI for the information and policy capacity of the state.

Both tracks welcome conceptual, normative and empirical contributions – qualitative or quantitative – from public administration, political science, management, information systems, and related fields. Comparative studies across countries, administrative levels, and policy areas are encouraged.

Relevance to the Conference Theme

This panel contributes to public management research by examining how AI reshapes core administrative functions, including knowledge production, information processing, professional judgment, decision-making, and citizen–state relations. The panel asks not only what AI does to public administration, but also what public administration must do to govern AI responsibly and responsively. In doing so, it connects digital transformation debates to broader questions of fairness, accountability, and administrative capacity.

Directly engaging the conference theme, the panel situates AI within the governance of grand challenges, overlapping crises, and constrained administrative capacity. While AI is increasingly viewed as a tool to strengthen government’s ability to address societal problems through enhanced prediction, coordination, and service delivery, its adoption raises critical challenges regarding accountability, explainability, surveillance, and control.

The panel explores whether and under what conditions AI can strengthen institutional problem-solving capacity, while remaining responsive to citizens' needs and aligned with democratic and bureaucratic values. The panel aims to advance our understanding of how public organizations can capture the benefits of automation while preserving expertise, transparency, legitimacy, and accountability.

By bringing together organizational and citizen-oriented perspectives, the panel highlights the trade-offs and governance dilemmas associated with AI adoption. It encourages a critical examination of AI’s effects on organizational capacity, public service delivery, trust, legitimacy, and citizen–state relations, contributing to debates on how public institutions can strengthen both administrative capacity and societal responsiveness in an era of polycrisis and increasing complexity.