  External audits of AI systems are increasingly recognized as a key mechanism
for AI governance. The effectiveness of an audit, however, depends on the
degree of access granted to auditors. Recent audits of state-of-the-art AI
systems have primarily relied on black-box access, in which auditors can only
query the system and observe its outputs. However, white-box access to the
system's inner workings (e.g., weights, activations, gradients) allows an
auditor to perform stronger attacks, more thoroughly interpret models, and
conduct fine-tuning. Meanwhile, outside-the-box access to training and
deployment information (e.g., methodology, code, documentation, data,
deployment details, findings from internal evaluations) allows auditors to
scrutinize the development process and design more targeted evaluations. In
this paper, we examine the limitations of black-box audits and the advantages
of white- and outside-the-box audits. We also discuss technical, physical, and
legal safeguards for performing these audits with minimal security risks. Given
that different forms of access can lead to very different levels of evaluation,
we conclude that (1) transparency regarding the access and methods used by
auditors is necessary to properly interpret audit results, and (2) white- and
outside-the-box access allow for substantially more scrutiny than black-box
access alone.
