Explainability, intepretability and sensitivity analysis

A growing research activity is developing for increasing interpretability of machine findings. When complex architectures are used, analysts are, in fact, exposed to the black-box effect. This seminar will review several methods used both in the machine learning and in the simulation community to make the black box more transparent. We shall discuss tools such as partial dependence functions, layerwise relevance propagation, as well as present several local and global sensitivity analysis methods, also proposing new tools and new findings on popular tools.



Contatto: piercesare.secchi@polimi.it