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DTSTAMP:20260421T090514Z
LOCATION:Bldg. 6 - Room 102
DTSTART;TZID=Europe/Stockholm:20260629T164000
DTEND;TZID=Europe/Stockholm:20260629T172000
UID:submissions.pasc-conference.org_PASC26_sess119_msa129@linklings.com
SUMMARY:From Scalable Workflows to Trustworthy AI: Fairness, Bias, and Rep
 roducibility on HPC
DESCRIPTION:Anjali Khantaal (CERN, MBZUAI)\n\nMy work at CERN openlab focu
 sed 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 found that issues that look pu
 rely technical, such as data partitioning, performance profiling, and repr
 oducibility, can shape how much we should trust AI results. In this talk, 
 I use that experience as a starting point to discuss a broader question: h
 ow can HPC help us tackle bias in algorithms and data, rather than simply 
 scale existing problems faster? I will reflect on how transparent tooling,
  cross-site validation, and better diagnostics can expose hidden failure m
 odes, reduce silent errors, and support fairer AI workflows in scientific 
 computing. Breaking HPC silos, in this sense, is not only about connecting
  disciplines. It is also about connecting performance, accountability, and
  fairness in the way we build, evaluate, and deploy AI systems.\n\nDomain:
  Climate, Weather, and Earth Sciences, Engineering, Life Sciences, Computa
 tional Methods and Applied Mathematics\n\nSession Chairs: Maria Grazia Giu
 ffreda (ETH Zurich / CSCS), Florina Ciorba (University of Basel), Marie-Ch
 ristine Sawley (ICES Foundation), and Maria Girone (CERN)\n\n
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