Machine Learning Explainability: 'Do's' and 'Don'ts'

Explaining the decision and behavior of machine learning models has become increasingly important, especially in industries such as finance, insurance and healthcare. But what does it mean to explain a model? When do we need to have that in place and when not? In this talk we will discuss such topics, and cover some requirements to make explainability successful in a complex organization.