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DTSTART;TZID=Europe/Stockholm:20260629T160000
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UID:submissions.pasc-conference.org_PASC26_sess119@linklings.com
SUMMARY:MS2D - Breaking the HPC Silos: Towards Fairness for Tackling Bias 
 in Algorithms and Data
DESCRIPTION:Access the recording\n\nOrganizer(s): Maria Grazia Giuffreda (
 ETH Zurich / CSCS), Florina M. Ciorba (University of Basel), Marie-Christi
 ne Sawley (ICES Foundation), and Maria Girone (CERN)\n\nIDEAS4HPC proposes
  a minisymposium on bias, fairness, and transparency in high performance c
 omputing (HPC), addressing ethical and societal challenges that arise as l
 arge-scale computational methods increasingly influence scientific discove
 ry, engineering, and public decision-making. The session brings together s
 peakers at the intersection of HPC, data, ethics, and domain science, high
 lighting how diversity of perspectives and interdisciplinary collaboration
  can improve the robustness, reproducibility, and societal impact of compu
 tational research. The first presentation offers an early-career perspecti
 ve through a hands-on case study on AI-driven digital twins for drought ea
 rly warning in the Alps, illustrating how HPC-enabled AI can support equit
 able, climate-relevant decision-making and environmental resilience. The s
 econd contribution examines the design of fair, carbon-aware, and socially
  responsible scheduling mechanisms for large-scale HPC infrastructures, dr
 awing on operational experience from the CERN Worldwide LHC Computing Grid
  and the SKA Science Regional Centre Network. The third presentation explo
 res human-centered approaches to fairness and transparency in AI-powered H
 PC 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 discipl
 inary silos through federated, FAIR, and fair approaches to Digital Twins,
  for domains ranging from health up to social sciences.\n\nFrom Scalable W
 orkflows to Trustworthy AI: Fairness, Bias, and Reproducibility on HPC\n\n
 My work at CERN openlab focused on evaluating open-source AI workflows for
  scientific digital twins on HPC systems using the interTwin/itwinai stack
 . While studying distributed training across JUWELS Booster and Vega, I fo
 und that issues that look purely technical, such as data partitioning, per
 form...\n\n\nAnjali Khantaal (CERN, MBZUAI)\n---------------------\nProACT
 : Fair and Carbon-Aware Scheduling for Responsible HPC\n\nHPC infrastructu
 res face increasing pressure to reduce emissions while continuing to suppo
 rt diverse and growing scientific workloads. Initiatives by major cloud pr
 oviders such as Google, Microsoft, and Amazon demonstrate that delaying or
  relocating flexible workloads can significantly reduce emissi...\n\n\nDen
 isa-Andreea Constantinescu (EPFL) and Steven Senator (Los Alamos National 
 Laboratory)\n---------------------\nBreaking the Disciplinary Silos: Feder
 ated, FAIR, and Fair Approaches for Virtual Human Twins in Health and Care
 \n\nFollowing the European Commission (EC) definition, a Virtual Human Twi
 n (VHT) is a digital representation of human health or disease across mult
 iple anatomical scales, from cells to organ systems. Built from computatio
 nal models and heterogeneous data, VHTs aim to simulate and predict physio
 logical ...\n\n\nLiesbet Geris (KU Leuven, VPH society)\n-----------------
 ----\nHumans, Machines, and the Space Between: Engineering Effective Human
 -AI Partnerships\n\nIntelligence augmentation through mixed-initiative sys
 tems promises to combine AI's computational efficiency with human contextu
 al expertise. In this talk, I will explore a framework for bridging human 
 and artificial intelligence through three core pillars: interpretability, 
 feedback, and augmentati...\n\n\nMennatallah El-Assady (ETH Zurich)\n\nDom
 ain: Climate, Weather, and Earth Sciences, Engineering, Life Sciences, Com
 putational Methods and Applied Mathematics\n\nSession Chairs: Maria Grazia
  Giuffreda (ETH Zurich / CSCS) and Florina Ciorba (University of Basel)
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