10 Mar 2022
ML Explainability in Actuarial Data Science: A Primer
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 upcoming 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.
Organised by the EAA - European Actuarial Academy GmbH.
Participants are expected to have, apart from actuarial basics, a preliminary grasp on Machine Learning and hypothesis testing as well as the R and/or Python languages.
Technical requirementsPlease check with your IT department if your firewall and computer settings support web session participation (the programme Zoom is used for the web session). Please also make sure that you are joining the web session with a stable internet connection.
Purpose and Nature