  Explainable machine learning provides tools to better understand predictive
models and their decisions, but many such methods are limited to producing
insights with respect to a single class. When generating explanations for
several classes, reasoning over them to obtain a complete view may be difficult
since they can present competing or contradictory evidence. To address this
issue we introduce a novel paradigm of multi-class explanations. We outline the
theory behind such techniques and propose a local surrogate model based on
multi-output regression trees -- called LIMEtree -- which offers faithful and
consistent explanations of multiple classes for individual predictions while
being post-hoc, model-agnostic and data-universal. In addition to strong
fidelity guarantees, our implementation supports (interactive) customisation of
the explanatory insights and delivers a range of diverse explanation types,
including counterfactual statements favoured in the literature. We evaluate our
algorithm with a collection of quantitative experiments, a qualitative analysis
based on explainability desiderata and a preliminary user study on an image
classification task, comparing it to LIME. Our contributions demonstrate the
benefits of multi-class explanations and wide-ranging advantages of our method
across a diverse set scenarios.

Machine Learning;Artificial Intelligence;Machine Learning