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This research highlights a critical risk where frontier AI models for weather forecasting are predominantly developed in the Global North, potentially automating and widening existing inequalities that disproportionately harm vulnerable regions. The authors argue that fostering resilience requires shifting toward climate digital public infrastructure, well-being-centric evaluation metrics, and collaborative knowledge production to ensure equitable outcomes globally.

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The Rise of AI in Weather and Climate Information and its Impact on Global Inequality

arXiv31 iulie 20261 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2603.05710v2 Announce Type: replace-cross Abstract: 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.

Authors:Amirpasha Mozaffari, Amanda Duarte, Lina Teckentrup, Stefano Materia, Gina E. C. Charnley, Lluis Palma, Eulalia Baulenas Serra, Dragana Bojovic, Paula Checchia, Aude Carreric, Francisco Doblas-Reyes

Abstract: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.

Atmospheric and Oceanic Physics (physics.ao-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2603.05710 [physics.ao-ph]

(or arXiv:2603.05710v2 [physics.ao-ph] for this version)

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