Advancements in Large language models (LLMs) have enabled a variety of downstream applications like story and interview script generation.However, recent research raised concerns about culture-related fairness issues in LLM-generated content.In this work, we identify and systematically investigate LLMs’ **insider-outsider bias**, a phenomenon where models position themselves as "insiders" of mainstream cultures during generation while externalizing less dominant cultures.We propose the ***InsideOut*** benchmark with 4,000 generation prompts and three evaluation metrics to quantify this bias through a *culturally situated interview script generation* task, in which an LLM is positioned as a reporter interviewing local people across 10 diverse cultures.Empirical evaluation on 5 state-of-the-art LLMs reveals that while models adopt insider tones in over 88% US-contexted scripts on average, they disproportionately default to "outsider" stances for non-Western cultures.To mitigate these biases, we propose *2 inference-time methods*: a baseline prompt-based **Fairness Intervention Pillars (FIP)** method, and a structured **Mitigation via Fairness Agents (MFA)** framework consisting of a Single-Agent (MFA-SA), a Hierarchical-Agent (MFA-HA), and an autonomous Agentic Planning (MFA-Plan) pipeline.Empirical results demonstrate that agent-based MFA methods achieve outstanding and robust performance in mitigating the insider-outsider bias:For instance, on the Cultural Alignment Gap (CAG) metric, *MFA-SA reduces bias in Llama model by 89.70 % and MFA-HA mitigates bias in Qwen by 82.54%*.These findings showcase the effectiveness of agent-based methods as a promising direction for mitigating biases in generative LLMs.