Professor, TU Dortmund
Katharina Morik received her doctorate from the University of Hamburg in 1981 and her habilitation from the TU Berlin in 1988. In 1991, she established the chair of Artificial Intelligence at the TU Dortmund. In 2011, she acquired the Collaborative Research Center SFB 876 "Providing Information by Resource-Constrained Data Analysis", of which she is the spokesperson. It consists of 12 projects and a graduate school for more than 50 Ph. D. students. She is a spokesperson of the Competence Center for Machine Learning Rhein Ruhr (ML2R) and coordinator of the four German competence centers for machine learning. She coordinates the German with the French Ai centers. She is the author of more than 200 publications in prestigious journals and conferences. She was a member of the editorial board of the journal "Machine Learning" and is currently one of the editors of the international journal "Data Mining and Knowledge Discovery". She was a founding member, Program Chair and Vice-Chair of the conference series IEEE International Conference on Data Mining (ICDM) and Program Chair of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). She was involved in numerous EU projects and has coordinated the MiningMart project. Together with Volker Markl, Katharina Morik heads the working group "Technological Pioneers" of the platform "Learning Systems and Data Science" of the Federal Ministry of Education and Research (BMBF). Prof. Morik has been a member of the Academy of Technical Sciences since 2015 and of the North Rhine-Westphalian Academy of Sciences and Arts since 2016. She has been awarded Fellow of the German Society of Computer Science GI e.V. in 2019.
Katharina is speaking at
AI technology connects increasing amounts of data and becomes more and more interconnected with other digital systems. To unlock its economic potential and use it to the benefit of our society however, people need to trust AI-based systems and applications. Trust in this context comprises different perspectives, like compliance to data protection rules and policies as well as ethical values and standards, or that an AI system reliably behaves correctly even in highly complex or rare situations. Accordingly, this track will present different activities and projects on the national and European level which deal with this fundamental aspect of trust in AI, and discuss some promising approaches to achieve it, ranging from standardization and certification to research topics like explainability and integrity by design.
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