Keynote Speakers
Full Professor and Chair of Open Source Intelligence at the University of the Bundeswehr Munich (UniBwM), Germany
Short Bio:
Bio
Prof. Eirini Ntoutsi is Full Professor and Chair of Open Source Intelligence at the University of the Bundeswehr Munich (UniBwM), Germany. Prior to this, she served as Full Professor and Chair of Artificial Intelligence at the Free University of Berlin and as Associate Professor of Intelligent Systems at Leibniz University Hannover. She conducted her postdoctoral research at LMU Munich, initially joining as an Alexander von Humboldt Fellow, and earned her Ph.D. (2008) in Computer Science from the University of Piraeus, Greece. She holds both a Diploma (2001) and a Master’s degree (2003) in Computer Engineering and Informatics from the University of Patras (CEID), Greece. Her research focuses on Artificial Intelligence and Machine Learning, with an emphasis on responsible AI—particularly fairness, explainability, and robustness to real-world challenges such as data imbalances and non-stationarity. She is an active member of the research community, regularly contributing to the organization of scientific events; a recent highlight is her role research track co-chair for ECML PKDD 2025. Her work is supported by national (DFG, Volkswagen Foundation, BMWi, BMBF) and European (ITN, H2020) funding programs.
Title: The Complex and evolving landscape of bias and fairness in AI
Abstract: As AI systems become deeply embedded in every aspect of our lives — impacting individuals and societies everywhere, at all times — the issues of bias and fairness in AI systems have taken center stage. From hiring algorithms, university admissions, and credit scoring to predictive policing, banking, and healthcare diagnostics, the consequences of unfair or biased AI can be profound. Fairness in AI is a complex, multi-dimensional challenge. Determining who is protected — and who is excluded — depends on legal, cultural, and contextual factors, raising complex questions around intersectionality and multi-discrimination. Fairness must be balanced against often competing objectives like accuracy, privacy, and explainability, often involving non-trivial trade-offs. As models scale in complexity — particularly with generative and multimodal systems — new risks emerge, including latent biases in data, representational harms, and increased model opacity. In this talk, we will go over the evolving landscape of bias and fairness in AI systems, highlighting recent progress in understanding, detecting, and mitigating bias in AI systems.
Full Professor and Acting Vice Dean of the Faculty of Science.
Department of Computing Science, University of Alberta,
Canada
Short Bio:
Bio
Dr. Eleni Stroulia is a Professor in the Department of Computing Science at the University of Alberta. From 2011-2016, she held the NSERC/AITF Industrial Research Chair on Service Systems Management with IBM. Her research adopts AI and machine-learning methods to address real-world problems in health and engineering. She has played leadership roles in the GRAND, AGE-WELL and CFN Networks of Centres of Excellence. She has secured more than 4M in funding as a sole investigator and has supervised more than 100 trainees, who have gone forward to stellar academic and industrial careers. Since 2021, she has been serving as the Acting Vice Dean of the Faculty of Science.
Title: Computing Science in the Real World: Reflections on Two Projects
Abstract: There are many types of research impact that computer science researchers can aspire to! In my work, I have consistently tried to deploy software in the service of real-world workflows to reduce human effort and improve outcomes. In this presentation, I will reflect on two recent projects, the challenges we have faced, and some lessons we have learned.