A Framework for Using and Evaluating LLMs as Surrogate Experts in Security Surveys: Reliability, Bias, and Implications
arXiv 2608.16893•5f65fc140d2b4ad38d8daaf55ca3308aa4170bc75f27c767e02c87c94f77560f
ai-governanceai-safetyartificial-intelligencebiasdigital-evidencefairnessfrontier-aihealth-informaticslarge-language-modelsllm-reliabilityprivacyresearchrisk-managementruntime-monitoringsecurity-surveyssoc-security-operations
Paper metadata
- arXiv ID
- 2608.16893
- Version
- Not specified by this published record
- Category
- Computer Science — Computers and Society (cs.CY)
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Evidence and limitations
- Source ID
- arxiv_cs_cy
- Record identifier
- 5f65fc140d2b4ad38d8daaf55ca3308aa4170bc75f27c767e02c87c94f77560f
- Enrichment time
- 2026-08-19T07:23:44Z
- AI-assisted enrichment
- Yes
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