Open Problems in Technical AI Governance
Abstract
AI progress is creating a growing range of risks and opportunities, but it is often unclear how they should be navigated. In many cases, the barriers and uncertainties faced are at least partly technical. Technical AI governance, referring to technical analysis and tools for supporting the effective governance of AI, seeks to address such challenges. It can help to (a) identify areas where intervention is needed, (b) identify and assess the efficacy of potential governance actions, and (c) enhance governance options by designing mechanisms for enforcement, incentivization, or compliance. In this paper, we explain what technical AI governance is, why it is important, and present a taxonomy and incomplete catalog of its open problems. This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
Cite
@article{arxiv.2407.14981,
title = {Open Problems in Technical AI Governance},
author = {Anka Reuel and Ben Bucknall and Stephen Casper and Tim Fist and Lisa Soder and Onni Aarne and Lewis Hammond and Lujain Ibrahim and Alan Chan and Peter Wills and Markus Anderljung and Ben Garfinkel and Lennart Heim and Andrew Trask and Gabriel Mukobi and Rylan Schaeffer and Mauricio Baker and Sara Hooker and Irene Solaiman and Alexandra Sasha Luccioni and Nitarshan Rajkumar and Nicolas Moës and Jeffrey Ladish and David Bau and Paul Bricman and Neel Guha and Jessica Newman and Yoshua Bengio and Tobin South and Alex Pentland and Sanmi Koyejo and Mykel J. Kochenderfer and Robert Trager},
journal= {arXiv preprint arXiv:2407.14981},
year = {2025}
}
Comments
Ben Bucknall and Anka Reuel contributed equally and share the first author position