AI in Climate Information Risks Widening North-South Divide
A recent study highlights the danger that the ongoing advancement of AI in weather and climate data may exacerbate the divide between the Global North and South. Most cutting-edge models originate from the Global North, leading to persistent inequalities in inputs, processes, and outputs—ranging from skewed training data to unrepresentative validation—impacting vulnerable areas disproportionately. The authors suggest establishing a Climate Digital Public Infrastructure, metrics focused on well-being, and collaborative knowledge creation to promote resilience instead of inequality. This research can be found on arXiv.
Key facts
- AI development in climate information risks automating the North-South divide.
- Frontier models are built almost exclusively in the Global North.
- Inequality persists through biased training data and unrepresentative validation.
- Vulnerable regions are disproportionately affected.
- Proposed solutions include Climate Digital Public Infrastructure.
- Evaluation metrics should center on well-being.
- Knowledge co-production is recommended to foster resilience.
- The study is available on arXiv.
Entities
Institutions
- arXiv
Locations
- Global North
- Global South