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Local surrogate approaches for explaining machine learning model predictionshave appealing properties, such as being model-agnostic and flexible in theirmodelling. Several methods exist that fit this description and share this goal.However, despite their shared overall procedure, they set out differentobjectives, extract different information from the black-box, and consequentlyproduce diverse explanations, that are -- in general -- incomparable. In thiswork we review the similarities and differences amongst multiple methods, witha particular focus on what information they extract from the model, as this haslarge impact on the output: the explanation. We discuss the implications of thelack of agreement, and clarity, amongst the methods' objectives on the researchand practice of explainability.

On the overlooked issue of defining explanation objectives for local-surrogate explainers