Session

DescriptionThis Chair’s minisymposium comprised of independent yet complimentary submissions brings together three perspectives on Responsible AI systems at scale: systems that are scientifically rigorous and societally trustworthy. The first speaker sets the stage by introducing how decisions are influenced by model predictions and how researchers use deep learning combined with Explainable Artificial Intelligence (XAI) techniques to enable transparent, realtime interpretability at scale with HPC. The second speaker sets addresses challenges in AI system design and evaluation, highlighting how benchmarking practices utilizing narrowly defined datasets may lead to misleading performance metrics, while also failing to make useful generalizations necessary for advancement in discovery science. The third speaker shares a European AI Factory for life sciences that at its core incorporates an AI-optimized supercomputing node, federated dataspace, and a trusted AI model validation sandbox. Finally, a moderated panel discussion will encourage further reflection on the challenges associated with designing, developing, and evaluating the efficacy of Responsible AI systems at scale.