AI对城市的评价受欧美文化影响，不具文化中立性
Culturally uneven urban perception in large language models
- 用全球街景数据测试LLM的城市感知，发现其偏向欧美视角
- AI评价比人类更单一，无法还原多元文化下的城市多样性
- 提示词可让AI接近特定区域人类观点，但会引入自我偏爱偏差
大型语言模型（LLMs）被广泛用于描述和评估城市，但其城市判断中的文化结构尚不清楚。本文提出一种测量框架，利用全球分层的街景图像数据集，检验基于LLM的城市感知是否具有文化中立性。三种前沿多模态模型生成的开放式描述与结构化评分均显示，中立基线更接近欧洲和北美地区的认知框架，而非其他文化视角。对比AI与人类的城市感知发现，提示词可使AI响应更贴近特定区域的人类描述，但无法恢复人类反应的丰富性和多样性，反而弱化了观察到的人口统计模式，并引入基于情感的自我偏好偏差。结果表明，将AI视为城市任务中的中立工具存在系统性风险，尤其在跨文化比较、评估或表征城市时。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used to describe and evaluate cities, yet the cultural structure of their urban judgments remains understudied. Here we introduce a measurement framework for testing whether LLM-based urban perception is culturally neutral, using a globally stratified street-view image dataset. Open-ended descriptions and structured scores generated by three frontier multimodal models all show that the neutral baseline lies closer to regional framings associated with Europe and North America than to other cultural framings. Comparisons between AI and human urban perception further show that prompting can move AI responses closer to specific regional human descriptions, but fails to recover the variety and diversity of human responses, flattening observed demographic patterns and introducing sentiment-based self-favouring bias. These results indicate a systematic risk in treating AI as a neutral tool for urban tasks, especially when model outputs are used to compare, evaluate or represent cities across cultural contexts.
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