Artificial Intelligence in Government: Why People Feel They Lose Control

TL;DR

This paper applies principal-agent theory to understand how delegating governmental tasks to AI affects public legitimacy. Through a pre-registered factorial survey across tax, welfare, and bail domains, it shows that while AI can improve perceived efficiency and trust, it simultaneously diminishes citizens' perceived control. When framing AI use around assessability, dependency, or contestability, both trust in government and sense of control decline notably, suggesting a 'failure-by-success' dynamic. The study argues that PAT provides a powerful predictive framework for AI governance and underscores the need for transparent delegation and accountability mechanisms to preserve democratic legitimacy in digital governance.

Abstract

The use of Artificial Intelligence (AI) in public administration is expanding rapidly, moving from automating routine tasks to deploying generative and agentic systems that autonomously act on goals. While AI promises greater efficiency and responsiveness, its integration into government functions raises concerns about fairness, transparency, and accountability. This article applies principal-agent theory (PAT) to conceptualize AI adoption as a special case of delegation, highlighting three core tensions: assessability (can decisions be understood?), dependency (can the delegation be reversed?), and contestability (can decisions be challenged?). These structural challenges may lead to a "failure-by-success" dynamic, where early functional gains obscure long-term risks to democratic legitimacy. To test this framework, we conducted a pre-registered factorial survey experiment across tax, welfare, and law enforcement domains. Our findings show that although efficiency gains initially bolster trust, they simultaneously reduce citizens' perceived control. When the structural risks come to the foreground, institutional trust and perceived control both drop sharply, suggesting that hidden costs of AI adoption significantly shape public attitudes. The study demonstrates that PAT offers a powerful lens for understanding the institutional and political implications of AI in government, emphasizing the need for policymakers to address delegation risks transparently to maintain public trust.

Figures (1)

• Figure 1: Density and bar plots for all three dependent variables with the treatment effects. The dashed line in the first two panels indicates the mean score of the human condition. For each condition, the estimates with 95%-CIs are shown.