@inproceedings{
anderljung2023towards,
title={Towards Publicly Accountable Frontier {LLM}s},
author={Markus Anderljung and Everett Smith and Joe O'Brien and Lisa Soder and Benjamin Bucknall and Emma Bluemke and Jonas Schuett and Robert Trager and Lacey Strahm and Rumman Chowdhury},
booktitle={Socially Responsible Language Modelling Research},
year={2023},
url={https://openreview.net/forum?id=4JNa7bg4tn}
}
With the increasing integration of frontier large language models (LLMs) into society and the economy, decisions related to their training, deployment, and use have far-reaching implications. These decisions should not be left solely in the hands of frontier LLM developers. LLM users, civil society and policymakers need trustworthy sources of information to steer such decisions for the better. Involving outside actors in the evaluation of these systems (external scrutiny) offers a solution: it can help provide information that is more accurate and complete. Despite encouraging signs of increasing external scrutiny of frontier LLMs, its success is not assured. In this paper, we survey six requirements for effective external scrutiny of frontier AI systems and organize them under the ASPIRE framework: Access, Searching attitude, Proportionality to the risks, Independence, Resources, and Expertise. We then illustrate how external scrutiny might function throughout the AI lifecycle.
Towards Publicly Accountable Frontier LLMs
Markus Anderljung Everett Smith Joe O'Brien Lisa Soder Benjamin Bucknall Emma Bluemke Jonas Schuett Robert Trager Lacey Strahm Rumman Chowdhury