  Since the behavior of a neural network model is adversely affected by a lack
of diversity in training data, we present a method that identifies and explains
such deficiencies. When a dataset is labeled, we note that annotations alone
are capable of providing a human interpretable summary of sample diversity.
This allows explaining any lack of diversity as the mismatch found when
comparing the \textit{actual} distribution of annotations in the dataset with
an \textit{expected} distribution of annotations, specified manually to capture
essential label diversity. While, in many practical cases, labeling (samples
$\rightarrow$ annotations) is expensive, its inverse, simulation (annotations
$\rightarrow$ samples) can be cheaper. By mapping the expected distribution of
annotations into test samples using parametric simulation, we present a method
that explains sample representation using the mismatch in diversity between
simulated and collected data. We then apply the method to examine a dataset of
geometric shapes to qualitatively and quantitatively explain sample
representation in terms of comprehensible aspects such as size, position, and
pixel brightness.
