Inspection of Blackbox Models for Evaluating Vulnerability in Maternal, Newborn, and Child Health

Inspection of Blackbox Models for Evaluating Vulnerability in Maternal, Newborn, and Child Health

William Ogallo, Skyler Speakman, Victor Akinwande, Kush R Varshney, Aisha Walcott-Bryant, Charity Wayua, Komminist Weldemariam

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

Improving maternal, newborn, and child health (MNCH) outcomes is a critical target for global sustainable development. Our research is centered on building predictive models, evaluating their interpretability, and generating actionable insights about the markers (features) and triggers (events) associated with vulnerability in MNCH. In this work, we demonstrate how a tool for inspecting "black box" machine learning models can be used to generate actionable insights from models trained on demographic health survey data to predict neonatal mortality.
Keywords:
Machine Learning: general