BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:ENBIS Spring Meeting 2027
DTSTART:20270331T060000Z
DTEND:20270401T163000Z
DTSTAMP:20260603T091900Z
UID:indico-event-93@conferences.enbis.org
CONTACT:office@enbis.org
DESCRIPTION:Welcome to the ENBIS Spring Meeting \n \nLinz\, Austria\, M
 arch 31- April 1\, 2027\nStatistics and Explainable AI\nIn an era of incre
 asing algorithmic complexity and growing demands for transparency\, accoun
 tability\, and trustworthiness in AI-driven decision-making\, the intersec
 tion of statistics and explainable AI (XAI) is becoming critically importa
 nt. This Spring Meeting aims to serve as a cross-disciplinary platform for
  researchers\, practitioners\, and industry experts to exchange cutting-ed
 ge developments in statistical methods that enable interpretable and expla
 inable AI systems. At its core\, the meeting focuses on how statistical th
 inking\, uncertainty quantification\, and rigorous experimental design can
  enhance the transparency and reliability of AI models\, both in theory an
 d in practice. We welcome contributions that advance traditional statistic
 al interpretability methods as well as those that push boundaries using no
 vel approaches to make black-box AI systems more transparent and accountab
 le.\nBeyond technical depth\, the conference will encourage discussions on
  strategic challenges including the deployment of XAI in safety-critical s
 ystems\, regulatory compliance (such as the EU AI Act)\, and the role of h
 uman-in-the-loop approaches in maintaining trust and interpretability. The
  goal of this Spring Meeting is to facilitate a dynamic exchange of ideas 
 that promotes scientific advancement and its effective translation into pr
 actical solutions\, fostering meaningful collaboration between statisticia
 ns\, AI researchers\, industry\, and society.\nTopics of interest include\
 , but are not limited to:\n\n\nInterpretable machine learning and statisti
 cal models\nLocal and global model explanation techniques (LIME\, SHAP\, e
 tc.)\nCausal inference and counterfactual explanations\nBayesian methods f
 or interpretable AI models\nUncertainty quantification and sensitivity ana
 lysis in XAI\nDesign of experiments for AI model validation and explainabi
 lity\nStatistical reliability and robustness of explanation methods\nTrust
 worthy AI and fairness of predictive models\nExplainable AI in medicine an
 d healthcare\nEvaluation and validation of XAI systems\nXAI for time serie
 s forecasting and process monitoring\nEthical\, legal\, and social implica
 tions of XAI\nTrust and transparency in AI-driven decision-making\n\n\nOrg
 anizing committee\nLocal organization comittee headed by Helmut Waldl\nSci
 entific program committee\nScientific comittee headed by Werner G. Müller
 \n\nhttps://conferences.enbis.org/event/93/
IMAGE;VALUE=URI:https://conferences.enbis.org/event/93/logo-2590257072.png
LOCATION:Linz\, Austria
URL:https://conferences.enbis.org/event/93/
END:VEVENT
END:VCALENDAR