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Impact-based Forecasts and Warnings (IbFWs) are crucial for disaster risk reduction; however, most systems focus on single hazards, overlooking the complex interactions of multi-risk events. This siloed approach can underestimate impacts, especially when hazards occur simultaneously or sequentially. Developing multi-risk IbFW systems requires interdisciplinary collaboration, improved modelling, more impact data and clearer terminology. Analysis of historical disasters shows that multi-hazard events cause disproportionate economic losses. Advancing IbFW systems demands pragmatic innovation, robust datasets, and inclusive strategies to better reflect real-world hazard complexity.

This Comment analyzes a global online landslide classification challenge to examine whether AI competitions can help participants engage with international standards. The results show that while technical innovation was strong, awareness of standards on transparency, sustainability, and bias was limited. The prototype used for this competition can serve as a scalable mechanism to foster the responsible use of AI for disaster risk reduction and beyond.