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

Planetary health is an emerging field which 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. Underlying these global challenges is the urgent need to effectively allocate scarce resources. My research develops data-driven AI decision-making methods to do so, overcoming the messy data ubiquitous in these settings. Here, I’ll present technical advances in stochastic bandits, robust reinforcement learning, and restless bandits, addressing research questions that emerge from my close collaboration with the public sector. I’ll also discuss bridging the gap from research and practice, including anti-poaching field tests in Cambodia, field visits in Belize and Uganda, and large-scale deployment with SMART conservation software.