This paper investigates the prevalence and mechanisms of social bias in Large Vision-Language Model (LVLM)-based text-to-image (T2I) models, revealing that system prompts are a key driver. It introduces FairPro, a training-free meta-prompting framework that enables LVLMs to dynamically generate fairness-aware system prompts, significantly reducing bias while maintaining image quality.