These days, nobody disputes the profound impact and yet-untouched potential of Machine Learning and Artificial Intelligence anymore. Yet, in the actuarial sciences, these breakthrough possibilities are hampered by regulation, the need for numerical confidence and insight into model decision making, the latter being subsumed as a "black box problem". Thus, the quest for explainability is in fact much more pressing than in any other industry.

This seminar on ML explainability aims to provide insights into the areas of unsupervised learning, supervised learning and artificial neural nets via model-agnostic explainability approaches.