大模型预测出行时放大种族性别偏见，少数群体更难关联高收入地点。
Popular LLMs Amplify Race and Gender Disparities in Human Mobility
- 用姓名带/不带身份信息的提示，测试三款大模型对人群出行的预测偏差。
- 少数族裔被显著低估与高收入地点的关联，女性被少关联职业相关地点。
- 揭示大模型如何复制并强化社会刻板印象，尤其在涉及种族性别时。
随着大语言模型（LLMs）越来越多地应用于影响社会结果的领域，理解其放大偏见的倾向至关重要。本研究考察了主流大模型（GPT-4、Gemini、Claude）在预测人类出行行为时是否存在基于种族和性别的偏见。通过分析包含不同姓名特征的提示，我们发现这些模型频繁反映并放大现有社会偏见：少数族裔个体被显著低估与财富相关地点（POIs）的关联；女性则被持续关联较少的职业相关地点，相比男性。这些偏差表明，大模型不仅映射社会刻板印象，还在实际应用中加剧了结构性不平等。
原文摘要 · Abstract (English)
As large language models (LLMs) are increasingly applied in areas influencing societal outcomes, it is critical to understand their tendency to perpetuate and amplify biases. This study investigates whether LLMs exhibit biases in predicting human mobility -- a fundamental human behavior -- based on race and gender. Using three prominent LLMs -- GPT-4, Gemini, and Claude -- we analyzed their predictions of visitations to points of interest (POIs) for individuals, relying on prompts that included names with and without explicit demographic details. We find that LLMs frequently reflect and amplify existing societal biases. Specifically, predictions for minority groups were disproportionately skewed, with these individuals being significantly less likely to be associated with wealth-related points of interest (POIs). Gender biases were also evident, as female individuals were consistently linked to fewer career-related POIs compared to their male counterparts. These biased associations suggest that LLMs not only mirror but also exacerbate societal stereotypes, particularly in contexts involving race and gender.
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