揭示AI模型中的地理偏见，提出评估生成内容地理多样性的方法。
Geographic Bias and Diversity in AI Evaluation
- 分析训练数据与模型设计导致的地理代表性偏差
- 发现生成AI倾向过度使用典型地点（默认地点）
- 提供多维度评估生成内容地理多样性的基准方法
在阻碍AI负责任发展与部署的诸多挑战中，偏见问题受到最广泛关注。这反映出研究人员普遍担忧：生成式AI等模型的输出可能承载结构性分布失衡（源于训练数据或模型设计），进而加剧社会不平等或在生物多样性、灾害应对等多个领域引入系统性扭曲。然而，关于地理偏见的研究仍较薄弱，缺乏可量化的评估基准。本文通过文献综述，考察基础模型重塑偏见研究格局的背景下，涵盖生成式与非生成式AI时期的相关工作。首先识别出多种地理偏见，包括训练数据中的代表性偏差、语言模型在事实回忆上的区域差异，以及生成式AI对典型地点（称为默认地点）的过度偏好。随后，展示近期研究如何通过认知层级、参数设置和输出模态等维度，评估生成式AI输出的地理多样性。
原文摘要 · Abstract (English)
Among the many challenges hindering the responsible development and deployment of AI, arguably none has faced more intense scrutiny than bias in its various forms. This underscores the widespread concerns across AI researchers that model outputs, e.g., from generative AI, may encode structural distributional imbalances (stemming from training data or model design) that may amplify social inequality or introduce systemic distortions across application domains ranging from biodiversity to disaster mitigation. Yet, relatively little work has investigated the geographical nature of bias or developed measurable benchmarks for what it means for (generative) AI to be unbiased. In this chapter, we investigate this issue through a literature review. As foundation models are reshaping the landscape of bias research, we examine work spanning both the pre-generative AI and generative AI periods. First, we identify a range of geographic biases. These biases span from representation bias in the training data and regional disparities in the factual recall of language models to the tendency of generative AI to over-proportionally favor prototypical places (called defaults). Then, we showcase how recent studies address the latter bias by evaluating geographic diversity in the outputs of generative AI across various cognitive levels, parameter settings, and output modalities.
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