MS5B – HPC for Society: Leveraging for Trust and Transparency
Session Chair
Event Type
Minisymposium
Climate, Weather, and Earth Sciences
Applied Social Sciences and Humanities
Engineering
Computational Methods and Applied Mathematics
TimeWednesday, July 114:00 – 16:00 CEST
LocationBldg. 6 – 003
DescriptionOrganizer(s): Tobias Hodel (University of Bern)
High-Performance Computing (HPC) is increasingly central to deploying trustworthy, transparent, and accountable AI systems in critical societal domains. This session brings together four contributions showing how advanced computational infrastructures strengthen public trust across areas such as health, agriculture, transportation, and energy.
The first speaker introduces how model predictions influence decisions and demonstrates how deep learning combined with Explainable AI (XAI) enables transparent, real-time interpretability at scale using HPC. The second speaker addresses challenges in AI system design and evaluation, highlighting how narrow benchmarking datasets can lead to misleading results and limit generalization.
The third presentation presents an HPC-enabled intelligent toll management framework integrating real-time computer vision with blockchain to ensure transparent, auditable, and tamper-resistant public infrastructure transactions. The fourth contribution introduces HP2C-DT, a multi-tier digital twin architecture for renewable energy systems, where HPC supports large-scale simulations, AI training, and probabilistic analysis to improve grid resilience and decision-making.
Together, these works show that HPC is not just about speed or scale, but a key enabler of transparency, robustness, and reproducibility. By embedding AI in secure and computationally rigorous frameworks, HPC helps build systems that society can trust.
High-Performance Computing (HPC) is increasingly central to deploying trustworthy, transparent, and accountable AI systems in critical societal domains. This session brings together four contributions showing how advanced computational infrastructures strengthen public trust across areas such as health, agriculture, transportation, and energy.
The first speaker introduces how model predictions influence decisions and demonstrates how deep learning combined with Explainable AI (XAI) enables transparent, real-time interpretability at scale using HPC. The second speaker addresses challenges in AI system design and evaluation, highlighting how narrow benchmarking datasets can lead to misleading results and limit generalization.
The third presentation presents an HPC-enabled intelligent toll management framework integrating real-time computer vision with blockchain to ensure transparent, auditable, and tamper-resistant public infrastructure transactions. The fourth contribution introduces HP2C-DT, a multi-tier digital twin architecture for renewable energy systems, where HPC supports large-scale simulations, AI training, and probabilistic analysis to improve grid resilience and decision-making.
Together, these works show that HPC is not just about speed or scale, but a key enabler of transparency, robustness, and reproducibility. By embedding AI in secure and computationally rigorous frameworks, HPC helps build systems that society can trust.
Presentations
