  Explainable artificial intelligence (XAI) is one of the most intensively
developed area of AI in recent years. It is also one of the most fragmented
with multiple methods that focus on different aspects of explanations. This
makes difficult to obtain the full spectrum of explanation at once in a compact
and consistent way. To address this issue, we present Local Universal Explainer
(LUX), which is a rule-based explainer that can generate factual,
counterfactual and visual explanations. It is based on a modified version of
decision tree algorithms that allows for oblique splits and integration with
feature importance XAI methods such as SHAP or LIME. It does not use data
generation in opposite to other algorithms, but is focused on selecting local
concepts in a form of high-density clusters of real data that have the highest
impact on forming the decision boundary of the explained model. We tested our
method on real and synthetic datasets and compared it with state-of-the-art
rule-based explainers such as LORE, EXPLAN and Anchor. Our method outperforms
the existing approaches in terms of simplicity, global fidelity,
representativeness, and consistency.
