Geographic Blind Spots in AI Control Monitors: A Cross-National Audit of Claude Opus 4.6
Artificial intelligence (AI) control protocols assume that trusted large language model (LLM) monitors reliably assess proposed actions across all deployment contexts. This paper tests that assumption in the geographic dimension. We audit Claude Opus 4.6-the monitor specified in Apart Research's AI Control Hackathon Track 3 benchmark-for systematic gaps in its factual knowledge of the global AI landscape. We develop the AI Control Knowledge Framework (ACKF), a six-dimension thematic scheme, and
Record details
Published: 20 March 2026
Source: arXiv
Category: Research
Topics: Transparency
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (20 March 2026), “Geographic Blind Spots in AI Control Monitors: A Cross-National Audit of Claude Opus 4.6,” evidence record 6958, https://ethics.ai/record/6958 (originally published by arXiv).
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