6 papers
FLEX: Feature Importance from Layered Counterfactual Explanations
Nawid Keshtmand, Roussel Desmond Nzoyem, Jeffrey Nicholas Clark
Machine learning models achieve state-of-the-art performance across domains, yet their lack of interpretability limits safe deployment in high-stakes settings. Counterfactual expla…
Machine Learning for Climate Policy: Understanding Policy Progression in the European Green Deal
Patricia West, Michelle WL Wan, Alexander Hepburn +3
Climate change demands effective legislative action to mitigate its impacts. This study explores the application of machine learning (ML) to understand the progression of climate p…
Uncertainty assessment in satellite-based greenhouse gas emissions estimates using emulated atmospheric transport
Jeffrey N. Clark, Elena Fillola, Nawid Keshtmand +2
Monitoring greenhouse gas emissions and evaluating national inventories require efficient, scalable, and reliable inference methods. Top-down approaches, combined with recent advan…
Prototype-enhanced prediction in graph neural networks for climate applications
Nawid Keshtmand, Elena Fillola, Jeffrey Nicholas Clark +2
Data-driven emulators are increasingly being used to learn and emulate physics-based simulations, reducing computational expense and run time. Here, we present a structured way to…
Improving Local Air Quality Predictions Using Transfer Learning on Satellite Data and Graph Neural Networks
Finn Gueterbock, Raul Santos-Rodriguez, Jeffrey N. Clark
Air pollution is a significant global health risk, contributing to millions of premature deaths annually. Nitrogen dioxide (NO2), a harmful pollutant, disproportionately affects ur…
Exploring the Requirements of Clinicians for Explainable AI Decision Support Systems in Intensive Care
Jeffrey N. Clark, Matthew Wragg, Emily Nielsen +7
There is a growing need to understand how digital systems can support clinical decision-making, particularly as artificial intelligence (AI) models become increasingly complex and…