Abstract. We propose a novel eXplainable AI algorithm to compute
faithful, easy-to-understand, and complete global decision rules from
local explanations for tabular data by combining XAI methods with
closed frequent itemset mining. Our method can be used with any
local explainer that indicates which dimensions are important for a
given sample for a given black-box decision. This property allows our
algorithm to choose among different local explainers, addressing the
disagreement problem, i.e., the observation that no single explanation
method consistently outperforms others across models and datasets.
Unlike usual experimental methodology, our evaluation also accounts for
the Rashomon effect in model explainability. To this end, we demonstrate
the robustness of our approach in finding suitable rules for nearly all of
the 700 black-box models we considered across 14 benchmark datasets.
The results also show that our method exhibits improved runtime, high
precision and F1-score while generating compact and complete rules.