Understanding how AI will represent and reason about geography should be a key concern for all of us, as the broader public increasingly interacts with spaces and places through these systems. Similarly, in line with the nature of foundation models, our own research often relies on pre-trained models. Hence, understanding what world AI systems construct is as important as evaluating their accuracy, including factual recall. To motivate the need for such studies, we provide three illustrative vignettes, i.e., exploratory probes, in the hope that they will spark lively discussions and follow-up work: (1) Do models form strong defaults, and how brittle are model outputs to minute syntactic variations? (2) Can distributional shifts resurface from the composition of individually benign tasks, e.g., when using AI systems to create personas? (3) Do we overlook deeper questions of understanding when solely focusing on the ability of systems to recall facts such as geographic principles?

翻译：理解人工智能将如何表征与推理地理知识，应当成为我们共同关注的核心议题，因为广大公众正日益通过这些系统与空间和场所进行互动。同样，基于基础模型的特性，我们自身的研究也常依赖预训练模型。因此，理解AI系统构建的世界图景，与评估其事实回忆等准确性同等重要。为激发此类研究的必要性，我们提供三个探索性案例作为说明性示例，以期引发热烈讨论与后续研究：（1）模型是否形成强烈的默认假设？输出结果对句法细微变化的敏感度如何？（2）个体无害任务的组合是否会导致分布偏移重现？例如在使用AI系统创建人物角色时。（3）当我们仅关注系统回忆地理原则等事实的能力时，是否忽略了更深层的理解问题？