An Explainable Forecasting System for Humanitarian Needs Assessment
Keywords:Humanitarian Response, Machine Learning, Graphical Models
AbstractWe present a machine learning system for forecasting forced displacement populations deployed at the Danish Refugee Council (DRC). The system, named Foresight, supports long term forecasts aimed at humanitarian response planning. It is explainable, providing evidence and context supporting the forecast. Additionally, it supports scenarios, whereby analysts are able to generate forecasts under alternative conditions. The system has been in deployment since early 2020 and powers several downstream business functions within DRC. It is central to our annual Global Displacement Report which informs our response planning. We describe the system, key outcomes, lessons learnt, along with technical limitations and challenges in deploying machine learning systems in the humanitarian sector.
How to Cite
Nair, R., Madsen, B., & Kjærum, A. (2023). An Explainable Forecasting System for Humanitarian Needs Assessment. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15569-15575. https://doi.org/10.1609/aaai.v37i13.26846
IAAI Technical Track on deployed Highly Innovative Applications of AI