Artificial intelligence in government: why people feel they lose control

Abstract

The use of Artificial Intelligence (AI) in public administration is expanding rapidly. While AI promises greater efficiency and responsiveness, its integration into government and administration raises concerns about fairness, transparency, and accountability. Relying on principal-agent theory and related frameworks, we conceptualize AI adoption as a case of algorithmic delegation involving power and information asymmetries. This perspective highlights three core tensions: assessability (can the delegate’s decisions be understood?), dependency (can the delegation be reversed?), and contestability (can the delegate’s 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. We conducted a pre-registered factorial survey experiment across tax, welfare, and law enforcement domains. Our findings show that although providing information about efficiency gains initially bolsters trust, it simultaneously reduces 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 delegation theory 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.