Speakers
Donatella Cea
Elisabeth Georgii
(Helmholtz Zentrum München)
Florian Kofler
(HAI)
Francesco Campi
Harshavardhan Subramanian
Helena Pelin
Lisa Barros de Andrade e Sousa
(Helmholtz AI)
Mahyar Valizadeh
(Helmholtz AI consultacy health unit)
Sabrina Benassou
Theresa Willem
Description
During this course participants will get an introduction to the topic of Explainable AI (XAI). The goal of the course is to help participants understand how XAI methods can help uncover biases in the data or provide interesting insights. After a general introduction to XAI, the course goes deeper into state-of-the-art model agnostic interpretation techniques as well as a practical session covering these techniques. Finally, we will focus on two model specific post-hoc interpretation methods, with hands-on training covering interpretation of random forests and neural networks with imaging data to learn about strengths and weaknesses of these standard methods used in the field.
