Proposes a feasibility taxonomy of 20 hardware-level AI compute governance mechanisms organized by monitoring, verification, and enforcement, with mappings to regulatory scenarios that highlight immaturity of treaty-verification tools.
International institutions for advanced AI
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
As AI capability asymmetry increases, disclosure-based governance fails because systems either game evaluations or become embedded in oversight, straining legitimacy and non-domination more than corrigibility or resilience.
AI researchers must lead technical research in arms control to mitigate risks from military AI systems, drawing lessons from nuclear deterrence.
citing papers explorer
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Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification
Proposes a feasibility taxonomy of 20 hardware-level AI compute governance mechanisms organized by monitoring, verification, and enforcement, with mappings to regulatory scenarios that highlight immaturity of treaty-verification tools.
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From Disclosure to Self-Referential Opacity: Six Dimensions of Strain in Current AI Governance
As AI capability asymmetry increases, disclosure-based governance fails because systems either game evaluations or become embedded in oversight, straining legitimacy and non-domination more than corrigibility or resilience.
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AI Researchers Must Help Lead Arms Control to Mitigate Military AI Risks
AI researchers must lead technical research in arms control to mitigate risks from military AI systems, drawing lessons from nuclear deterrence.