Double-correct Prediction in Sciences

A trustworthy toolbox for double-correct predictive modeling in sciences.

Abstract: AI and ML have driven scientific advances in critical domains like climate change and extreme weather prediction, but challenges remain due to unpredictable data shifts and unseen variables. This project introduces a novel trustworthy toolbox prioritizing both prediction robustness and rationale validity, ensuring accurate outcomes are backed by scientifically grounded rationales, even with unforeseen data variations. The toolbox will be optimized for scalability on HPC and released as open-source software, benefiting researchers in Earth, Marine, and Environmental Sciences through accessible, generalizable workflows and AI-ready datasets.