What Should Frontier AI Developers Disclose About Internal Deployments?

TL;DR

This paper proposes a framework for frontier AI developers to disclose key information about internal model deployments to enhance safety, transparency, and governance.

Contribution

It identifies specific disclosure categories and analyzes their benefits, limitations, and risk mitigation strategies, filling a guidance gap.

Findings

Framework covers capabilities, usage, safety, governance.

Disclosures can improve transparency and safety oversight.

Guidance applicable to public and private reporting.

Abstract

Frontier AI developers are increasingly deploying highly capable models internally to automate AI R&D, but these deployments currently face limited external oversight. It is essential, therefore, that developers provide evidence that internally deployed models are safe. While recent work has highlighted the risks of internal deployments and proposed broad approaches to transparency and governance, there remains little guidance on the specific information developers should disclose about them. We address this gap by identifying key information that companies should disclose about internally deployed models across four categories: capabilities, usage, safety mitigations, and governance. For each category, we analyse the key benefits and limitations of disclosure and consider how disclosure-related risks can be mitigated. Our framework could be used by developers to inform both public transparency documents, such as model system cards, and private periodic reports required under emerging frontier AI regulation.