  Surrogate explainers of black-box machine learning predictions are of
paramount importance in the field of eXplainable Artificial Intelligence since
they can be applied to any type of data (images, text and tabular), are
model-agnostic and are post-hoc (i.e., can be retrofitted). The Local
Interpretable Model-agnostic Explanations (LIME) algorithm is often mistakenly
unified with a more general framework of surrogate explainers, which may lead
to a belief that it is the solution to surrogate explainability. In this paper
we empower the community to "build LIME yourself" (bLIMEy) by proposing a
principled algorithmic framework for building custom local surrogate explainers
of black-box model predictions, including LIME itself. To this end, we
demonstrate how to decompose the surrogate explainers family into
algorithmically independent and interoperable modules and discuss the influence
of these component choices on the functional capabilities of the resulting
explainer, using the example of LIME.
