  We attempt to give a unifying view of the various recent attempts to (i)
improve the interpretability of tree-based models and (ii) debias the the
default variable-importance measure in random Forests, Gini importance. In
particular, we demonstrate a common thread among the out-of-bag based bias
correction methods and their connection to local explanation for trees. In
addition, we point out a bias caused by the inclusion of inbag data in the
newly developed explainable AI for trees algorithms.
