  Constructing accurate model-agnostic explanations for opaque machine learning
models remains a challenging task. Classification models for high-dimensional
data, like images, are often inherently complex. To reduce this complexity,
individual predictions may be explained locally, either in terms of a simpler
local surrogate model or by communicating how the predictions contrast with
those of another class. However, existing approaches still fall short in the
following ways: a) they measure locality using a (Euclidean) metric that is not
meaningful for non-linear high-dimensional data; or b) they do not attempt to
explain the decision boundary, which is the most relevant characteristic of
classifiers that are optimized for classification accuracy; or c) they do not
give the user any freedom in specifying attributes that are meaningful to them.
We address these issues in a new procedure for local decision boundary
approximation (DBA). To construct a meaningful metric, we train a variational
autoencoder to learn a Euclidean latent space of encoded data representations.
We impose interpretability by exploiting attribute annotations to map the
latent space to attributes that are meaningful to the user. A difficulty in
evaluating explainability approaches is the lack of a ground truth. We address
this by introducing a new benchmark data set with artificially generated Iris
images, and showing that we can recover the latent attributes that locally
determine the class. We further evaluate our approach on tabular data and on
the CelebA image data set.
