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Explainable artificial intelligence (XAI) is one of the most intensivelydeveloped area of AI in recent years. It is also one of the most fragmentedwith multiple methods that focus on different aspects of explanations. Thismakes difficult to obtain the full spectrum of explanation at once in a compactand 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 ofdecision tree algorithms that allows for oblique splits and integration withfeature importance XAI methods such as SHAP. It limits the use data generationin opposite to other algorithms, but is focused on selecting local concepts ina form of high-density clusters of real data that have the highest impact onforming the decision boundary of the explained model and generating artificialsamples with novel SHAP-guided sampling algorithm. We tested our method on realand synthetic datasets and compared it with state-of-the-art rule-basedexplainers such as LORE, EXPLAN and Anchor. Our method outperforms the existingapproaches in terms of simplicity, fidelity, representativeness, andconsistency.

Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanations