Session

DescriptionIDEAS4HPC proposes a minisymposium on bias, fairness, and transparency in high performance computing (HPC), addressing ethical and societal challenges that arise as large-scale computational methods increasingly influence scientific discovery, engineering, and public decision-making. The session brings together speakers at the intersection of HPC, data, ethics, and domain science, highlighting how diversity of perspectives and interdisciplinary collaboration can improve the robustness, reproducibility, and societal impact of computational research. The first presentation offers an early-career perspective through a hands-on case study on AI-driven digital twins for drought early warning in the Alps, illustrating how HPC-enabled AI can support equitable, climate-relevant decision-making and environmental resilience. The second contribution examines the design of fair, carbon-aware, and socially responsible scheduling mechanisms for large-scale HPC infrastructures, drawing on operational experience from the CERN Worldwide LHC Computing Grid and the SKA Science Regional Centre Network. The third presentation explores human-centered approaches to fairness and transparency in AI-powered HPC workflows, focusing on human–AI collaboration, explainable AI, and data visualization to enable bias detection, model understanding, and informed human decision-making. The fourth presentation addresses breaking disciplinary silos through federated, FAIR, and fair approaches to Digital Twins, for domains ranging from health up to social sciences.