  The majority of existing post-hoc explanation approaches for machine learning
models produce independent, per-variable feature attribution scores, ignoring a
critical inherent characteristics of homogeneously structured data, such as
visual or text data: there exist latent inter-variable relationships between
features. In response, we develop a novel model-agnostic and permutation-based
feature attribution approach based on the relational analysis between input
variables. As a result, we are able to gain a broader insight into the
predictions and decisions of machine learning models. Experimental evaluations
of our framework in comparison with state-of-the-art attribution techniques on
various setups involving both image and text data modalities demonstrate the
effectiveness and validity of our method.
