The emergence of large language models (LLMs) represents a major advance in artificial intelligence (AI) research. However, the widespread use of LLMs is also coupled with significant ethical and social challenges. Previous research has pointed towards auditing as a promising governance mechanism to help ensure that AI systems are designed and deployed in ways that are ethical, legal, and technically robust. However, existing auditing procedures fail to address the governance challenges posed by LLMs, which are adaptable to a wide range of downstream tasks. To help bridge that gap, Mokander will present a novel blueprint for how to audit LLMs.

Jakob Mökander is a visiting scholar at the Center for Information Technology Policy.