Jul 2026· Transforming Government: People, Process and Policy· pp. 1-16· 0 citations· 25 references
TL;DR
Assessing whether confidence in the US federal government’s ability to effectively regulate AI predicts citizens’ willingness to entrust AI with important decision-making responsibilities suggests that institutional trust may be an important condition for the democratic legitimacy and public acceptance of digital transformations.
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.
Introduction Artificial intelligence (AI) is increasingly used in public agencies to route inquiries, screen eligibility, support caseworkers, and automate routine service encounters. Citizen acceptance of these services depends on their links to public authority, accountability, and visible opportunities for human recourse. This study examines a trust-based mechanism connecting institutional trust, risk perception, AI service trust, and behavioral intention in China's digital government context. Methods The study combined an LLM-driven agent simulation involving 936 agents across three independent seeds, a 3 × 3 factorial scenario experiment involving 900 simulated agents, and a human-validation pilot using the same questionnaire and scenario structure. The pilot generated 189 submitted records, of which 182 were retained after attention checking. Results In the synthetic calibration, institutional trust is positively associated with AI service trust (IT → AST β = 0.607) and negatively associated with risk perception (IT → RP β = −0.271); risk perception is negatively associated with AI service trust (RP → AST β = −0.459); and AI service trust is positively associated with behavioral intention (AST → BI β = 0.424). The same directional pattern appears in the human-validation pilot (IT → AST β = 0.357; IT → RP β = −0.240; RP → AST β = −0.513; AST → BI β = 0.650). Scenario means also align with the simulation pattern (Pearson r = 0.803 for AST and r = 0.875 for BI across the nine cells), with the lowest pilot AST (3.667) and BI (3.413) in the fully automated high-risk condition. Discussion The findings connect confidence in government institutions with service-specific trust and indicate that perceived risk constrains acceptance of AI-enabled public services. In high-stakes automated settings, visible arrangements for human review may be necessary for AI service trust to translate into intended use. Public-sector AI acceptance is therefore shaped jointly by institutional credibility, perceived risk, and service encounter design.
Huihui Wang, Shixin Zhu· Frontiers in Psychology· 0 citations
This study aims to examine how institutional legitimacy and governance conditions shape public acceptance of artificial intelligence (AI)-based threat detection systems, demonstrating that technical accuracy is necessary but normatively insufficient for sustainable policy-oriented support.
A survey-based research design was used, with data from 510 valid respondents in South Korea. The study applied a two-step structural equation modeling approach comprising confirmatory factor analysis and structural path analysis, alongside bias-corrected bootstrap mediation analysis with 5,000 resamples.
Trust in government significantly predicts institutional legitimacy (ß = 0.660, p < 0.001), which, in turn, shapes both performance expectancy and perceived social deterrence. Direct path analysis confirmed 11 of 12 hypotheses, and bootstrap analysis verified significant indirect effects for all major antecedents. The model explains 84.8% of variance in behavioral intention (R² = 0.848). Ethical concern did not directly undermine legitimacy, indicating conditional rather than automatic normative resistance.
This research advances public-sector AI governance theory by positioning institutional legitimacy as a mediating filter and introducing perceived social deterrence as a policy-relevant cognitive mediator. It proves that governance readiness – not technical accuracy alone – determines durable policy support. Practically, it highlights the need for transparent oversight to secure public authorization. A limitation is its reliance on a South Korean general public sample, warranting future cross-national, multi-stakeholder comparative research.
Policymakers should complement technical development with governance mechanisms such as transparency, accountability and procedural safeguards.
The study underscores the societal importance of legitimacy-based governance when deploying high-risk AI systems affecting public safety.
This research advances public-sector AI governance theory by structurally positioning institutional legitimacy as a mediating evaluative filter between normative antecedents and cognitive expectations. It introduces perceived social deterrence as a policy-relevant cognitive mediator and provides empirical evidence that governance readiness – not technical accuracy alone – determines whether AI systems receive durable policy-oriented authorization.
Overall, AI-assisted governance offers substantial potential to strengthen accountability and stakeholder trust when supported by robust ethical safeguards, transparency measures, and clearly defined responsibility structures.
M. Mar, Ing. Nikolai Fabian Sebastián Yucra Añazco, Delia Nieves Coaquira Pari· Journal of Organizational an...· 0 citations
A normative analysis of thirteen recent studies on the challenges of technology implementation, ethical trust, and legal regulation suggests that the current governance dilemma stems not only from technological limitations but also from institutional neglect, which enables accountability avoidance.
This study synthesises technical and stakeholder dimensions of AI in property valuation using a structured qualitative approach via SLR and proposes a novel hybrid framework that integrates stakeholder trust factors with model precision to enhance both reliability and acceptance of AI tools.
Wajhat Ali, D. Samarasinghe, Zhenan Feng et al.· Urbanization, Sustainability...· 0 citations
Artificial intelligence is increasingly promoted as a tool for modernizing public administration, accelerating decision-making, improving public services, and reducing administrative costs. Yet, in heterogeneous Global South contexts shaped by structural inequality, technological dependency, unequal access to digital infrastructure, and uneven institutional capacity, algorithmic efficiency may also generate new forms of democratic exclusion. This article develops a normative conceptual analysis of AI governance and argues that public uses of AI should not be evaluated primarily through technical efficiency, ethical compliance, or procedural safeguards, but through democratic legitimacy. It proposes the concept of democratic algorithmic legitimacy, understood as a relational property of the sociotechnical and institutional arrangements through which public authority is exercised with the support of AI. Such arrangements are legitimate when their purposes and operation can be publicly justified to affected persons, when those persons have meaningful opportunities to influence and contest their use, and when responsible institutions retain the authority and capacity to review decisions, repair unjustified harms, modify systems, suspend their operation, or withdraw them when necessary. The framework operationalizes this standard through seven interdependent dimensions: transparency, participation, inclusion, accountability, contestability, correctability, and social justice. This conceptual architecture distinguishes technical performance from democratic authority and explains why efficient outcomes cannot compensate automatically for exclusion, opacity, weak accountability, inaccessible contestation, or ineffective correction. The article identifies interconnected structural, institutional, social, and democratic risks associated with AI deployment in unequal sociotechnical environments and outlines a governance agenda based on meaningful public participation, democratic impact assessment, independent scrutiny, institutional guarantees of explanation, review and appeal, protection of affected groups, public control, technological capacity, and context-sensitive regulation. The article concludes that AI governance should be assessed not only by what computational systems optimize, but by whether societies retain the democratic authority to shape, question, supervise, correct, and, when necessary, reject their use.
A. Duche-Pérez, Marco Tulio Falconí Picardo, Emmanuel Neptalí Augusto Chávez Urquizo et al.· Frontiers in Political Scien...· 0 citations
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