Speaker
Description
Due to their predictive accuracy, machine learning models are
increasingly employed in high-stakes decision making and in scientific
contexts. These predictive capabilities often come at the price of
interpretability: The models are too complex to be inherently
intelligible by their human users.
In an attempt to restore this interpretability, a broad range of methods
have been developed, often subsumed under the umbrella of the term
Explainable AI (XAI). In this session, we will give the attendees an
introduction to the field. We will start by mapping the different
approaches using the standard taxonomies, before we formally introduce
some of the most popular explanation methods.
Over the course of the session we will focus on the core challenges that
practitioners face: (1) Translating the outputs of XAI methods into
concise statements about model and data, and (2) choosing the
explanation technique that is most suitable for a given task. We will
tackle these questions with a particular focus on the task of learning
about feature target associations, thereby preparing attendees to employ
XAI methods in their own work.