The reciprocity of data integration in disaster risk analysis

Humanitarian organizations are increasingly challenged by the amount of data available to drive their decisions. Useful data can come from many sources, exists in different formats, and merging it into a basis for analysis and planning often exceeds organizations’ capacities and resources. At the same time, affected communities’ participation in...

Effects of data ambiguity and cognitive biases on the interpretability of machine learning models in humanitarian decision making

The effectiveness of machine learning algorithms depends on the quality and amount of data and the operationalization and interpretation by the human analyst. In humanitarian response, data is often lacking or overburdening, thus ambiguous, and the time-scarce, volatile, insecure environments of humanitarian activities are likely to inflict...