Trustworthy data science, what does it involve?

Jacqueline Asscher (Kinneret College)

Trustworthy data science, what does it involve?


Louise Bloch (Faculty of Computer Science, FH Dortmund)

Michael Kamp (Institute for Artificial Intelligence in Medicine, University Duisburg-Essen)

Michael Kirchhof (Max-Planck Research School, University of Tübingen)

Olivier Roustant (INSA Toulouse, University of Toulouse)

Magdalena Wischnewski (Research Center Trustworthy Data Science and Security, UA Ruhr)


Chair: Jacqueline Asscher (Kinneret College)

Date: 13th March 2024, at 12:00-13:00 CET

Advanced statistical and machine learning models as well as adaptive and intelligent methods are becoming increasingly important in applied data science. At the same time, their trustworthiness is critical for the progress and adoption of data science applications in various fields, especially in industry. The webinar will consist of five short teaser presentations by experts who will share their perspectives on trustworthy data science, followed by a discussion with the audience. The topics covered range from the importance of explainability (Louise Bloch) to trustworthy machine learning (Michael Kamp) in medicine and the interpretation of black box models depending on the availability of derivatives (Olivier Roustant). The importance of easy-to-handle uncertainties (Michael Kirchhof) is discussed and trust in autonomous driving systems is examined from a psychological perspective (Magdalena Wischnewski). 

The webinar is organised by Sonja Kuhnt (FH Dortmund) and Markus Pauly (TU Dortmund), who are hosting the ENBIS Spring Meeting 2024 on Trustworthy Data Science.



    • 12:00 PM 1:00 PM
      Trustworthy data science, what does it involve? 1h
      Speakers: Louise Bloch, Magdalena Wischnewski, Michael Kamp, Michael Kirchhof, Olivier Roustant