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

TL;DR

The paper investigates how so-called protective AI technologies—privacy-enhancing tools, safety evaluations, and alignment methods—can function as anti-regulatory instruments that shape both current and future regulation. Using Wu's framework of avoidance and change, it categorizes Encryption, Decentralization, and Synthetic Data as mechanisms of avoidance, and Open-Source models, AI evaluations, and alignment as mechanisms of change. It emphasizes that framing and rhetoric around these technologies can divert regulatory focus toward voluntary standards and self-governance, potentially undermining binding rules. The work calls for policy attention to underlying business incentives and technical governance to ensure protections are genuine and aligned with public interests, rather than serving as regulatory workarounds.

Abstract

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.