Abstract

Abstract

The IUCN Red List of Threatened Species is one of the world’s most important conservation resources–often referred to as a “Barometer of Life”. It provides a standardised, evidence-based assessment framework for grouping species into extinction risk categories (from Least Concern to Critically Endangered) using quantitative criteria. The Red List is thus a critical indicator of the health of the world’s biodiversity, and guides policy and conservation action worldwide. However, Red List coverage is constrained by funding and availability of trained assessors. This results in significant data gaps (for example, fewer than 2% of invertebrates have been assessed), and a long tail of outdated assessments (over 25% are at least 10 years old). In this talk, I’ll share initial research into how agentic AI could support the Red List workflow. I’ll present results showing that AI coding agents can reliably pass the official Red List assessor training exam–and, crucially, explain their answers with citations to official guidelines. I’ll also demonstrate how I leverage agentic coding to rapidly develop and maintain a real-time “evidence-base” dashboard, integrating live citizen science observations with relevant scientific literature. I’ll close by outlining plans for next steps and future research directions, and then open the floor to questions and highly-welcomed feedback.

Bio

Shane Weisz is a first-year PhD student in Computer Science at the University of Cambridge, supervised by Professor Anil Madhavapeddy. His research focuses on AI to support global biodiversity conservation.