🤖 AI Summary
This study addresses the challenge of designing compliance mechanisms for probabilistic AI systems in public administration under conditions of political alternation, ensuring their auditability, reproducibility, and legal legitimacy while guarding against strategic misuse by subsequent political actors. It introduces, for the first time, the concept of an “alignment surface” within an AI governance framework that explicitly accounts for government turnover. Drawing on game-theoretic and institutional modeling approaches, the paper formally characterizes the dynamic interactions among government agencies, AI systems, and political successors. The analysis reveals that AI-driven compliance reforms initially intended to enhance regulatory efficacy may, through automation expansion and rule entrenchment, inadvertently heighten long-term risks of political manipulation, thereby offering a theoretical warning about potentially irreversible governance trajectories.
📝 Abstract
Governments are increasingly interested in using AI to make administrative decisions cheaper, more scalable, and more consistent. But for probabilistic AI to be incorporated into public administration it must be embedded in a compliance layer that makes decisions reviewable, repeatable, and legally defensible. That layer can improve oversight by making departures from law easier to detect. But it can also create a stable approval boundary that political successors learn to navigate while preserving the appearance of lawful administration. We develop a formal model in which institutions choose the scale of automation, the degree of codification, and safeguards on iterative use. The model shows when these systems become vulnerable to strategic use from within government, why reforms that initially improve oversight can later increase that vulnerability, and why expansions in AI use may be difficult to unwind. Making AI usable can thus make procedures easier for future governments to learn and exploit.