  Interpretable surrogates of black-box predictors trained on high-dimensional
tabular datasets can struggle to generate comprehensible explanations in the
presence of correlated variables. We propose a model-agnostic interpretable
surrogate that provides global and local explanations of black-box classifiers
to address this issue. We introduce the idea of concepts as intuitive groupings
of variables that are either defined by a domain expert or automatically
discovered using correlation coefficients. Concepts are embedded in a surrogate
decision tree to enhance its comprehensibility. First experiments on FRED-MD, a
macroeconomic database with 134 variables, show improvement in
human-interpretability while accuracy and fidelity of the surrogate model are
preserved.
