  Local surrogate approaches for explaining machine learning model predictions
have appealing properties, such as being model-agnostic and flexible in their
modelling. Several methods exist that fit this description and share this goal.
However, despite their shared overall procedure, they set out different
objectives, extract different information from the black-box, and consequently
produce diverse explanations, that are -- in general -- incomparable. In this
work we review the similarities and differences amongst multiple methods, with
a particular focus on what information they extract from the model, as this has
large impact on the output: the explanation. We discuss the implications of the
lack of agreement, and clarity, amongst the methods' objectives on the research
and practice of explainability.
