AI气候模型加剧南北不平等，需共建全球公平数字基础设施。
The Rise of AI in Weather and Climate Information and its Impact on Global Inequality
- AI气候模型多由北方国家主导，数据与评估偏倚放大南半球弱势地区风险。
- 现有模型训练与验证缺乏代表性，导致发展中国家预报精度显著偏低。
- 建议构建气候数字公共基础设施，推动共研共治以增强韧性。
当前人工智能的发展轨迹正加剧全球气候信息体系中的南北差距。前沿模型几乎全部在北半球研发，这种不平等贯穿于输入、处理和输出环节：从有偏的训练数据到缺乏代表性的验证，对脆弱地区造成不成比例的影响。解决这些差异需要建设气候数字公共基础设施，采用以福祉为中心的评估指标，并推动知识共生产，以促进韧性而非加深不公。
原文摘要 · Abstract (English)
AI development's current trajectory risks automating and amplifying the North-South divide in the global climate information system. Frontier models are built almost exclusively in the Global North, and this inequality continues through inputs, processes, and outputs, from biased training data to unrepresentative validation, disproportionately affecting vulnerable regions. Addressing these disparities requires a Climate Digital Public Infrastructure, evaluation metrics centring well-being, and knowledge co-production to foster resilience rather than inequity.
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