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TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
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DTSTART:19700308T020000
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DTSTART:19701101T020000
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BEGIN:VEVENT
DTSTAMP:20260521T070019Z
LOCATION:Bldg. 6 - Room 104
DTSTART;TZID=Europe/Stockholm:20260630T134500
DTEND;TZID=Europe/Stockholm:20260630T154500
UID:submissions.pasc-conference.org_PASC26_sess146@linklings.com
SUMMARY:MS3F - Responsible AI Systems at Scale for Life Sciences and Socie
 ty
DESCRIPTION:This Chair’s minisymposium comprised of independent yet compli
 mentary submissions brings together three perspectives on Responsible AI s
 ystems at scale: systems that are scientifically rigorous and societally t
 rustworthy. 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 enab
 le transparent, realtime interpretability at scale with HPC. The second sp
 eaker sets addresses challenges in AI system design and evaluation, highli
 ghting 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 in
 corporates an AI-optimized supercomputing node, federated dataspace, and a
  trusted AI model validation sandbox. Finally, a moderated panel discussio
 n will encourage further reflection on the challenges associated with desi
 gning, developing, and evaluating the efficacy of Responsible AI systems a
 t scale.\n\nBeyond Benchmark Performance: Trustworthy AI, Generalization, 
 and Translational Impact in Scientific Computing\n\nArtificial intelligenc
 e (AI) and machine learning are increasingly used within high-performance 
 computing workflows to accelerate scientific discovery, particularly in da
 ta-intensive domains such as drug discovery. However, strong benchmark per
 formance does not guarantee scientific or translational ...\n\n\nSally Ell
 ingson (University of Kentucky)\n---------------------\nResponsible AI Sys
 tems at Scale Panel Discussion\n\ntbd\n\n\nElaine M. Raybourn (University 
 of Central Florida)\n---------------------\nBuilding Trust in AI-Driven Sy
 stems: Scalable and Explainable Deep Learning Frameworks for Health and Ag
 riculture on HPC\n\nTransparency and interpretability are essential for de
 ploying AI systems in domains where decisions affect health, safety, and f
 ood security. This talk presents an integrated approach to building trustw
 orthy AI models for image-based classification by combining deep learning 
 with Explainable Artific...\n\n\nNatasha Nigar (UET, Lahore)\n------------
 ---------\n1HealthAI, an AI Factory Oriented to Support Data Processing fo
 r the Global Health\n\n1HealthAI (European One-Health AI Factory) is an Sp
 anish specialized AI Factory dedicated to the One Health domain (integrati
 ng human, animal, and environmental health). Hosted by the Galicia Superco
 mputing Center (CESGA) and co-led by the Spanish National Research Council
  (CSIC), the consortium inc...\n\n\nIsabel Campos (IFCA/CSIC)\n\nDomain: L
 ife Sciences, Computational Methods and Applied Mathematics\n\nSession Cha
 ir: Elaine M. Raybourn (University of Central Florida)
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