High-stakes decisions from low-quality data: AI decision-making for planetary health

Abstract

Planetary health recognizes the inextricable link between human health and the health of our planet. Our planet’s growing crises include biodiversity loss, with animal population sizes declining by an average of 70% since 1970, and maternal mortality, with 1 in 49 girls in low-income countries dying from complications in pregnancy or birth. Overcoming these crises requires effectively allocating and managing our limited resources. This dissertation develops data-driven AI decision-making methods to do so, overcoming the messy data ubiquitous in these settings. Here, we present technical advances in multi-armed bandits, reinforcement learning, and causal inference, addressing research questions that emerged from on-the-ground challenges across conservation and maternal health. These methods enable practitioners to take efficient and robust actions necessary for planetary health. Specifically, this work was done in close partnership with NGOs, government agencies, and other stakeholders to deploy AI for on-the-ground intervention efforts in biodiversity conservation, where these methods have been integrated with a system used by 1,200 protected areas worldwide, and maternal health, working with the world's largest mobile health program to reduce maternal mortality in India.