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Open Problems in Technical AI Governance
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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.
Forward citations
Cited by 15 Pith papers
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Exposure is not manifestation: measurement target and output resolution jointly determine which behavioural-faithfulness evaluator wins
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Macro-Prudential AI Governance: A Two-Layer Early Warning and Response System for Frontier AI
A Basel-III-style two-layer system—coordinated finder-coordinator-defender reporting plus ECAR, CRTH, and ARS buffers—can detect and dampen correlated risk build-up across frontier AI labs’ internal deployments.
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The Foreign Policy AI Evaluation Gap
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Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms
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No Certificate, No Categorical Speech Act: A Brouwerian Assertibility Constraint for Public Reason
An AI may assert or deny high-stakes claims only when it can exhibit a publicly contestable certificate; otherwise it is obligated to return Undetermined.
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