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Open Problems in Technical AI Governance

Authors :
Reuel, Anka
Bucknall, Ben
Casper, Stephen
Fist, Tim
Soder, Lisa
Aarne, Onni
Hammond, Lewis
Ibrahim, Lujain
Chan, Alan
Wills, Peter
Anderljung, Markus
Garfinkel, Ben
Heim, Lennart
Trask, Andrew
Mukobi, Gabriel
Schaeffer, Rylan
Baker, Mauricio
Hooker, Sara
Solaiman, Irene
Luccioni, Alexandra Sasha
Rajkumar, Nitarshan
Moës, Nicolas
Ladish, Jeffrey
Guha, Neel
Newman, Jessica
Bengio, Yoshua
South, Tobin
Pentland, Alex
Koyejo, Sanmi
Kochenderfer, Mykel J.
Trager, Robert
Publication Year :
2024

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.<br />Comment: Ben Bucknall and Anka Reuel contributed equally and share the first author position

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2407.14981
Document Type :
Working Paper