Position: Require Frontier AI Labs To Release Small "Analog" Models
This policy targets the AI safety-innovation tradeoff problem for regulators and the broader research community, offering a novel regulatory framework rather than incremental technical improvements.
The paper proposes a regulatory policy requiring frontier AI labs to release small, open-access analog models distilled from their large proprietary models to address AI safety concerns while promoting innovation. This approach enables broad community participation in safety verification and interpretability research, with minimal additional costs, aiming to reduce regulatory burdens and accelerate safety advancements.
Recent proposals for regulating frontier AI models have sparked concerns about the cost of safety regulation, and most such regulations have been shelved due to the safety-innovation tradeoff. This paper argues for an alternative regulatory approach that ensures AI safety while actively promoting innovation: mandating that large AI laboratories release small, openly accessible analog models (scaled-down versions) trained similarly to and distilled from their largest proprietary models. Analog models serve as public proxies, allowing broad participation in safety verification, interpretability research, and algorithmic transparency without forcing labs to disclose their full-scale models. Recent research demonstrates that safety and interpretability methods developed using these smaller models generalize effectively to frontier-scale systems. By enabling the wider research community to directly investigate and innovate upon accessible analogs, our policy substantially reduces the regulatory burden and accelerates safety advancements. This mandate promises minimal additional costs, leveraging reusable resources like data and infrastructure, while significantly contributing to the public good. Our hope is not only that this policy be adopted, but that it illustrates a broader principle supporting fundamental research in machine learning: deeper understanding of models relaxes the safety-innovation tradeoff and lets us have more of both.