Systems based on artificial intelligence and machine learning models should
be transparent, in the sense of being capable of explaining their decisions to
gain humans' approval and trust. While there are a number of explainability
techniques that can be used to this end, many of them are only capable of
outputting a single one-size-fits-all explanation that simply cannot address
all of the explainees' diverse needs. In this work we introduce a
model-agnostic and post-hoc local explainability technique for black-box
predictions called LIMEtree, which employs surrogate multi-output regression
trees. We validate our algorithm on a deep neural network trained for object
detection in images and compare it against Local Interpretable Model-agnostic
Explanations (LIME). Our method comes with local fidelity guarantees and can
produce a range of diverse explanation types, including contrastive and
counterfactual explanations praised in the literature. Some of these
explanations can be interactively personalised to create bespoke, meaningful
and actionable insights into the model's behaviour. While other methods may
give an illusion of customisability by wrapping, otherwise static, explanations
in an interactive interface, our explanations are truly interactive, in the
sense of allowing the user to "interrogate" a black-box model. LIMEtree can
therefore produce consistent explanations on which an interactive exploratory
process can be built