Uncertainty at Scale: How CS Hinders Climate Research

Abstract:
Computer science is a powerful tool for enabling data-driven advances in global ecology and conservation. However, the amplification cuts two ways, as mechanisation can also compound problems inherent with just how uncertain anything to do with natural ecosystems is! Species habitat datasets are uncertain, local observations are uncertain, the resulting inferences about species distributions are uncertain, and side effects from interventions are uncertain; conservation action has evolved to take this into account. Computer science when applied without consideration of these factors can amplify the uncertainty by running ever-larger datasets through increasingly complex data pipelines and algorithms, all built upon wobbly foundations. What exact version of the dataset is being used? What exact version of the dataset did you use? What assumptions went into generating that dataset? What libraries, system dependencies and environment variables were used to calculate the results? In this talk, we first segment sources of uncertainty across ecological data sources and computation over them, and then reflect on how these uncertainties impact ecological research and how we might cleanly bound the uncertainty for future conservation research.
Bio:
Patrick Ferris is a research assistant in the Energy and Environment Group at the Department of Computer Science, University of Cambridge. He works alongside colleagues in Plant Sciences, Ecology and Zoology to better understand climate change, forests and biodiversity.
He is particularly interested in:
How technology can help tackle climate change (e.g. geocaml) and potential issues it creates.
The changing landscape of human rights as a consequence of technology and climate change.
Communicating science effectively to a wide audience (e.g. NI Forests) and improving diversity and inclusion in tech (see Outreachy).