Explainable machine learning models for humanitarian response - overview
65 million people are globally displaced, the highest ever in human history. Humanitarian aid budgets are also the largest they have been, yet only ~20% of aid recipients feel their needs have been met. The intern will contribute to the ongoing effort in humanitarian needs assessment by building models that leverage a multitude of data sources from humanitarian agencies to estimate mixed migration flows. The estimates need to be accompanied by explanations and empirical evidence that can provide valuable context to responding agencies.
1) Experience with machine learning and forecasting,
2) Previous experience applying machine learning to small samples
3) Knowledge of explainability in machine learning