Neutral Is Not Unbiased: Evaluating Implicit and Intersectional Identity Bias in LLMs Through Structured Narrative Scenarios

Large Language Models often reproduce societal biases, yet most evaluations overlook how such biases evolve across nuanced contexts or intersecting identities. We introduce a scenario-based evaluation framework built on 100 narrative tasks, designed to be neutral at baseline and systematically modified with gender and age cues. Grounded in the theory of Normative-Narrative Scenarios, our approach provides ethically coherent and socially plausible settings for probing model behavior. Analyzing responses from five leading LLMs-GPT-4o, LLaMA 3.1, Qwen2.5, Phi-4, and Mistral-using Critical Discourse Analysis and quantitative linguistic metrics, we find consistent evidence of bias. Gender emerges as the dominant axis of bias, with intersectional cues (e.g., age and gender combined) further intensifying disparities. Our results underscore the value of dynamic narrative progression for detecting implicit, systemic biases in Large Language Models.