arXiv:2604.20048v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to describe, evaluate and interpret places, yet it remains unclear whether they do so from a culturally neutral standpoint. Here we test urban perception in frontier LLMs using a balanced global street-view sample and prompts that either remain neutral or invoke different regional cultural standpoints. Across open-ended descriptions and structured place judgments, the neutral condition proved not to be neutral in practice. Prompts associated with Europe and Northern America remained systemati
As large language models become increasingly integrated into diverse applications for describing and evaluating complex real-world phenomena like cities, understanding their inherent biases and perspectives is critical.
This research reveals a significant and embedded cultural bias within frontier LLMs, demonstrating that their 'neutral' perspectives are often skewed towards Western cultural standpoints.
Developers and end-users of LLMs must now explicitly acknowledge and account for culturally uneven baselines in AI perceptions, impacting everything from urban planning and social policy to marketing and international relations.
- · AI ethicists and researchers
- · Regions and cultures underrepresented in training data
- · AI developers focused on bias mitigation
- · AI systems aiming for global neutrality without correction
- · Applications relying on unexamined LLM 'objectivity'
- · Regions implicitly overrepresented and thus distorting global perspectives
This finding will accelerate efforts to diversify training data and develop more culturally agnostic AI perception models.
Governments and international organizations may push for standards or regulatory frameworks to address cultural biases in widely deployed AI systems.
Increased awareness of AI's cultural lens could foster new markets for AI localization and culturally sensitive AI interpretation services.
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