Signal AI Governance Under Political Turnover: The Alignment Surface of Compliance Design (arXiv:2604.21103)

Summary

An arXiv preprint (2604.21103), submitted April 22, 2026 by Andrew J. Peterson, formally models how AI systems embedded in government compliance frameworks become vulnerable to strategic exploitation during political transitions. The model examines three institutional choices: the scale of automation, the degree of codification, and safeguards on iterative use. Peterson writes that "making AI usable can thus make procedures easier for future governments to learn and exploit," producing a paradox in which "reforms that initially improve oversight can later increase that vulnerability." The paper argues that compliance layers built for transparency and legal defensibility can become stable boundaries that political successors learn to navigate while maintaining the appearance of lawful administration.