Large Language Models (LLMs) used in creative workflows can reinforce stereotypes and perpetuate inequities, making fairness auditing essential. Existing methods rely on constrained tasks and fixed benchmarks, leaving open-ended creative outputs unexamined. We introduce the Persona Brainstorm Audit (PBA), a scalable and easy to extend auditing method for bias detection across multiple intersecting identity and social roles in open-ended persona generation. PBA quantifies bias using degree-of-freedom-aware normalized Cramér's V, producing interpretable severity labels that enable fair comparison across models and dimensions. Applying PBA to 12 LLMs (120,000 personas, 16 bias dimensions), we find that bias evolves nonlinearly across model generations: larger and newer models are not consistently fairer, and biases that initially decrease can resurface in later releases. Intersectional analysis reveals disparities hidden by single-axis metrics, where dimensions appearing fair individually can exhibit high bias in combination. Robustness analyses show PBA remains stable under varying sample sizes, role-playing prompts, and debiasing prompts, establishing its reliability for fairness auditing in LLMs.

翻译：在创意工作流中使用的大语言模型（LLM）可能强化刻板印象并延续不平等现象，这使得公平性审计至关重要。现有方法依赖于受限任务和固定基准，未能对开放式创意输出进行检验。本文提出人物构思审计（PBA），这是一种可扩展且易于扩展的审计方法，用于检测开放式人物生成中多重交叉身份与社会角色的偏见。PBA通过自由度感知的归一化Cramér's V量化偏见，生成可解释的严重程度标签，从而实现跨模型与跨维度的公平比较。将PBA应用于12个LLM（12万个人物，16个偏见维度）后发现：偏见随模型代际呈非线性演化——更大更新的模型并非持续更公平，且初期减弱的偏见可能在后续版本中重新显现。交叉性分析揭示了单维度指标所掩盖的差异：单独表现公平的维度在组合状态下可能呈现高偏见。鲁棒性分析表明PBA在不同样本量、角色扮演提示和去偏见提示下保持稳定，证实了其在LLM公平性审计中的可靠性。