Institutional trust and public support for AI-driven governance: implications for democratic resilience

Abstract

With the growing use of artificial intelligence (AI) in public governance, understanding public willingness to delegate decision-making authority to algorithmic systems has become a key issue. While prior research has examined the relationship between trust in public institutions and trust in AI, the role of institutional trust in shaping willingness to delegate high-stakes decisions to AI remains understudied. This study aims to address this gap using nationally representative survey data from Wave 152 of the Pew Research Center’s American Trends Panel (August 2024, n = 5,410). The study uses weighted logistic regression to assess whether confidence in the US federal government’s ability to effectively regulate AI predicts citizens’ willingness to entrust AI with important decision-making responsibilities. The analysis is based on 2,940 valid responses after excluding non-substantive answers. The findings demonstrate that institutional trust is a statistically significant predictor of support for algorithmic delegation. Higher levels of confidence in governmental AI regulation were associated with substantially higher odds of supporting the delegation of important decisions to AI systems (OR = 1.33; 95% CI [1.19, 1.50]; p < 0.001). Although utilitarian evaluations of personal benefit exert the strongest influence, institutional trust remains significant even after controlling for sociodemographic, informational, affective factors and political predispositions. The cross-sectional design and reliance on self-reported measures limit causal inference. The dependent variable captures normative willingness to delegate rather than the observed behavior, which is appropriate given that institutional-level AI use in higher domains is still emerging. Nevertheless, the use of national survey weights and extensive controls enhances the robustness of the findings. The results contribute to the literature on digital governance by identifying institutional trust as an independent legitimacy mechanism in the acceptance of algorithmic authority. For policymakers, the findings suggest that public support for AI-driven governance depends not only on the performance or perceived benefits of AI systems but also on citizens’ confidence in governmental regulatory capacity. Given that AI awareness was independently associated with higher support for delegation (OR = 1.36), strengthening institutional transparency, regulatory credibility and public AI literacy may be essential for sustainable AI implementation. As governments increasingly rely on algorithmic systems in high-stakes domains, the findings suggest that institutional trust may be an important condition for the democratic legitimacy and public acceptance of digital transformations. This study advances research on AI governance by empirically demonstrating that institutional trust in regulatory competence functions as an independent political condition for delegating authority to algorithmic systems. Unlike prior work that examines institutional trust as one predictor among many or that measures cross-national trust differences without testing the delegation pathway, this paper theorizes institutional regulatory trust as the central legitimacy mechanism and uses normative willingness to delegate, rather than abstract approval, as the outcome.