As artificial intelligence (AI) becomes increasingly embedded in government operations, retaining democratic control over these technologies is becoming ever more crucial for mitigating potential biases or lack of transparency. However, while much has been written about the need to involve citizens in AI deployment in public administration, little is known about how democratic control of these technologies works in practice.This chapter proposes to address this gap through participatory governance, a subset of governance theory that emphasises democratic engagement, in particular through deliberative practices. We begin by introducing the opportunities and challenges the AI use in government poses. Next, we outline the dimensions of participatory governance and introduce an exploratory framework which can be adopted in the AI implementation process. Finally, we explore how these considerations can be applied to AI governance in public bureaucracies. We conclude by outlining future directions in the study of AI systems governance in government.

Oversight mechanisms, whereby the functioning and behaviour of AI systems are controlled to ensure that they are tuned to public benefit, are a core aspect of human-centered AI. They are especially important in public sector AI applications, where decisions on core public services such as education, benefits, and child welfare have significant impacts. Much current thinking on oversight mechanisms revolves around the idea of human decision makers being present ‘in the loop’ of decision making, such that they can insert expert judgment at critical moments and thus rein in the functioning of the machine. While welcome, we believe that the theory of human in the loop oversight has yet to fully engage with the idea that decision making, especially in high-stakes contexts, is often currently made by hierarchical teams rather than one individual. This raises the question of how such hierarchical structures can effectively engage with an AI system that is either supporting or making decisions. In this position paper, we outline some of the key contemporary elements of hierarchical decision making in contemporary public services and show how they relate to current thinking about AI oversight, thus sketching out future research directions for the field.