The social embeddedness of trust in AI: How existing trust relations to decision-makers and institutions influence trust in AI decision aids for public administration

AI decision aids are increasingly adopted in public administration to support complex decisions tra-ditionally carried out by employees of public authorities. While prior research has emphasized deci-sion-makers’ trust in AI, less attention has been paid to stakeholders who are exposed to and af-fected by these emerging AI-supported decision-making processes and outcomes. In such con-texts, it is not only the AI itself – its process, performance, and purpose – that is assessed for trustworthiness, but also existing constellations of decision-makers and institutions that govern decision-making. We argue that trust in AI is socially embedded. Drawing on sociological theories of trust, we propose a framework that conceptualizes trust in AI decision aids as shaped by existing trust relations with decision-makers and institutions involved in decision-making – the ‘shadow of the past’. To explore this, we examine a case study of an AI-augmented geographic information system (AI-GIS) developed to support spatial planning for onshore wind energy in the course of sustainably energy transition dynamics in Germany. Based on 38 interviews with stakeholders from seven groups involved in spatial planning and wind energy development, we analyze initial (mis)trust in the AI-GIS. Using a combination of qualitative comparative analysis (QCA) and qualitative content analysis, we identify four distinct configurations that condition stakeholders’ (mis)trust. Each re-flects a unique interplay of interpersonal and institutional trust relations. The study offers a more nuanced understanding of trust in AI as a relational, context-dependent phenomenon, highlighting the relevance of institutions and existing trust relations for understanding and guiding AI adoption. It therefore directly contributes to the literature on sustainability transitions and their place-specific dynamics. AI systems are considered viable technical solutions for the transformation of energy, water, or food systems. Accordingly, trust in these AI systems needs to be understood as highly context-dependent: Trust is developed and experienced within specific institutional set-tings, regulatory cultures, and histories of technology adoption. Hence, our paper di-rects attention to who trusts what AI, where, and under what institutional arrangements and urges this to be a central question in the sustainability transitions literature.