  Entity matching in Customer 360 is the task of determining if multiple
records represent the same real world entity. Entities are typically people,
organizations, locations, and events represented as attributed nodes in a
graph, though they can also be represented as records in relational data. While
probabilistic matching engines and artificial neural network models exist for
this task, explaining entity matching has received less attention. In this
demo, we present our Explainable Entity Matching (xEM) system and discuss the
different AI/ML considerations that went into its implementation.
