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With the increasing integration of frontier large language models (LLMs) intosociety and the economy, decisions related to their training, deployment, anduse have far-reaching implications. These decisions should not be left solelyin the hands of frontier LLM developers. LLM users, civil society andpolicymakers need trustworthy sources of information to steer such decisionsfor the better. Involving outside actors in the evaluation of these systems -what we term 'external scrutiny' - via red-teaming, auditing, and externalresearcher access, offers a solution. Though there are encouraging signs ofincreasing external scrutiny of frontier LLMs, its success is not assured. Inthis paper, we survey six requirements for effective external scrutiny offrontier AI systems and organize them under the ASPIRE framework: Access,Searching attitude, Proportionality to the risks, Independence, Resources, andExpertise. We then illustrate how external scrutiny might function throughoutthe AI lifecycle and offer recommendations to policymakers.

Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework