# AI Governance under Political Turnover: The Alignment Surface of Compliance Design

arXiv: https://arxiv.org/abs/2604.21103 · HTML: https://mlsanity.com/paper/2604.21103/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 60 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models](https://mlsanity.com/paper/2201.03544/) (2022)
- [Inverse Reward Design](https://mlsanity.com/paper/1711.02827/) (2017)
- [Oversight Structures for Agentic AI in Public-Sector Organizations](https://mlsanity.com/paper/2506.04836/) (2025)

## Abstract (arXiv)

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.