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Navigating the Challenges of Automated Forecasting in Humanitarian Action
This chapter explores the development of automated prediction systems aimed at improving anticipatory actions in humanitarian contexts. It emphasizes the necessity for better data, model explainability, and the integration of machine learning with traditional forecasting, while highlighting the critical role of trust and actionable insights through a case study with the Kenyan Red Cross.