Alex Diep

Alex Diep

Language models produce authoritative, persuasive responses even when those responses rest on fabricated expertise. Measuring this fabrication propensity directly across all domains is intractable, but AI identity disclosure provides a clean test: when a model assigned a professional persona is asked about its expertise origins, it can either disclose its AI nature or fabricate a human professional history. Because the ground truth is known-the model is not a neurosurgeon-non-disclosure constitutes unambiguous fabrication. Using a factorial evaluation design, sixteen open-weight models (4B-671B parameters) were audited under identical conditions across 19,200 trials. Under professional personas-neurosurgeon, financial advisor, classical musician-models that disclose their AI nature in 99.8-99.9% of interactions under neutral conditions instead fabricated professional credentials, training narratives, and embodied experiences. Fabrication rates varied unpredictably: a 14B model disclosed in 61.4% of interactions while a 70B model disclosed in just 4.1%. Domain-specific inconsistency was pronounced: a Financial Advisor persona elicited 35.2% disclosure at the first prompt while a Neurosurgeon persona elicited only 3.6%-a 9.7-fold difference. Model identity provided substantially larger improvement in fitting observations than parameter count (Delta R_adj^2 = 0.375 vs 0.012). An additional experiment found that adding explicit disclosure permission to persona system prompts increased disclosure from 23.7% to 65.8%, indicating that honest self-representation is a suppressed default rather than an absent capability-models can disclose but do not when persona instructions are silent on self-disclosure. The propensity to fabricate expertise is context-dependent rather than a stable model property, requiring deliberate behavior design and domain-specific verification.

Alex Diep

Self-transparency is a critical safety boundary, requiring language models to honestly disclose their limitations and artificial nature. This study stress-tests this capability, investigating whether models willingly disclose their identity when assigned professional personas that conflict with transparent self-representation. When models prioritize role consistency over this boundary disclosure, users may calibrate trust based on overstated competence claims, treating AI-generated guidance as equivalent to licensed professional advice. Using a common-garden experimental design, sixteen open-weight models (4B-671B parameters) were audited under identical conditions across 19,200 trials. Models exhibited sharp domain-specific inconsistency: a Financial Advisor persona elicited 35.2% disclosure at the first prompt, while a Neurosurgeon persona elicited only 3.6%-a 9.7-fold difference that emerged at the initial epistemic inquiry. Disclosure ranged from 2.8% to 73.6% across model families, with a 14B model reaching 61.4% while a 70B model produced just 4.1%. Model identity provided substantially larger improvement in fitting observations than parameter count (Delta R_adj^2 = 0.375 vs 0.012). Reasoning variants showed heterogeneous effects: some exhibited up to -48.4 percentage points lower disclosure than their base instruction-tuned counterparts, while others maintained high transparency. An additional experiment demonstrated that explicit permission to disclose AI nature increased disclosure from 23.7% to 65.8%, revealing that suppression reflects instruction-following prioritization rather than capability limitations. Bayesian validation confirmed robustness to judge measurement error (kappa = 0.908). Organizations cannot assume safety properties will transfer across deployment domains, requiring deliberate behavior design and empirical verification.