Conversations In Water: Will AI Widen or Close the Global Water Information Gap?

Access to reliable water data, on availability, quality, use, and risk, remains deeply unequal across the world. Well-resourced regions increasingly draw on satellite monitoring, sensor networks, and machine learning to forecast droughts, floods, and contamination, while many water-stressed communities still lack even basic monitoring infrastructure. AI promises to close this gap: it can fill data voids with remote sensing and modelling, extend the reach of sparse ground networks, and make existing data more usable for decision-making. But the same technology could deepen the divide, favouring institutions with the computing power, technical expertise, and clean training data to deploy it, while leaving data-poor regions further behind and dependent on tools built without their context in mind.This collection invites compelling opinion articles and perspectives that explore the contradictions, trade-offs, and possibilities within this debate. We seek voices from across disciplines and backgrounds who can offer diverse perspectives on whether, and under what conditions, AI narrows or widens global disparities in water information. Contributions might address data infrastructure and access, algorithmic bias and representativeness, capacity building and technology transfer, the role of open data and open-source tools, or the governance structures needed to ensure AI serves water-insecure regions rather than bypassing them. Topic editors: Frontiers In Water Source: https://www.frontiersin.org/research-topics/84447/conversations-in-water-will-ai-widen-or-close-the-global-water-information-gap