Interpretable Machine Learning Approaches to Prediction of Chronic Homelessness

The model, HIFIS-RNN-MLP, incorporates both static and dynamic features of a client's history to forecast chronic homelessness 6 months into the client's future, achieving state-of-the-art performance and improved stakeholder trust of what is usually a "black box" neural network model through interpretable AI.