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
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative 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.
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.