LEX-EC: A Lexical Evidence-Channel Audit Framework for Zero-Shot LLM Personality Classification in Black-Box Settings
Large language models may easily assign personality labels from text, but model interpretability remains an open problem. To address this gap, we introduce LEX-EC, a reusable black-box audit framework combining prevalence and agreement diagnostics with controlled lexical ablation to distinguish marginal-distribution effects from trait-associated signal recoverable under restricted evidence. Using this framework, we illustrate how various text genres may exhibit sharply different profiles: free-f
Record details
Published: 27 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Safety & alignment · Healthcare · Transparency
Retrieved: 28 July 2026
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ethics.ai (27 July 2026), “LEX-EC: A Lexical Evidence-Channel Audit Framework for Zero-Shot LLM Personality Classification in Black-Box Settings,” evidence record 14049, https://ethics.ai/record/14049 (originally published by arXiv cs.AI).
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