RelEx: A Model-Agnostic Relational Model Explainer

Understand

In recent years, considerable progress has been made on improving the interpretability of machine learning models.

• This is essential, as complex deep learning models with millions of parameters produce state of the art results, but it can be nearly impossible to explain their predictions.
• While various explainability techniques have achieved impressive results, nearly all of them assume each data instance to be independent and identically distributed (iid).
• This excludes relational models, such as Statistical Relational Learning (SRL), and the recently popular Graph Neural Networks (GNNs), resulting in few options to explain them.