# Anti-Regulatory AI: How "AI Safety" is Leveraged Against Regulatory Oversight

arXiv: https://arxiv.org/abs/2509.22872 · HTML: https://mlsanity.com/paper/2509.22872/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2025.

## Built on by (most cited first, 3 of 3)

- [Big AI is accelerating the metacrisis: What can we do?](https://mlsanity.com/paper/2512.24863/) (2025)
- [Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective](https://mlsanity.com/paper/2601.22434/) (2026)
- [Position: Privacy Is a Claim, Not a Property of Synthetic Data](https://mlsanity.com/paper/2609.01273/) (2026)

## Abstract (arXiv)

AI companies increasingly develop and deploy privacy-enhancing technologies, bias-constraining measures, evaluation frameworks, and alignment techniques -- framing them as addressing concerns related to data privacy, algorithmic fairness, and AI safety. This paper examines the ulterior function of these technologies as mechanisms of legal influence. First, we examine how encryption, federated learning, and synthetic data -- presented as enhancing privacy and reducing bias -- can operate as mechanisms of avoidance with existing regulations in attempts to place data operations outside the scope of traditional regulatory frameworks. Second, we investigate how emerging AI safety practices including open-source model releases, evaluations, and alignment techniques can be used as mechanisms of change that direct regulatory focus towards industry-controlled voluntary standards and self-governance. We term this phenomenon "anti-regulatory AI" -- the deployment of ostensibly protective technologies that simultaneously shapes the terms of regulatory oversight. Our analysis additionally reveals how technologies' anti-regulatory functions are enabled through framing that legitimizes their deployment while obscuring their use as regulatory workarounds. This paper closes with a discussion of policy implications that centers on the consideration of business incentives that drive AI development and the role of technical expertise in assessing whether these technologies fulfill their purported protections.