  The concept of trustworthy AI has gained widespread attention lately. One of
the aspects relevant to trustworthy AI is robustness of ML models. In this
study, we show how to probabilistically quantify robustness against naturally
occurring distortions of input data for tree-based classifiers under the
assumption that the natural distortions can be described by multivariate
probability distributions that can be transformed to multivariate normal
distributions. The idea is to extract the decision rules of a trained
tree-based classifier, separate the feature space into non-overlapping regions
and determine the probability that a data sample with distortion returns its
predicted label. The approach is based on the recently introduced measure of
real-world-robustness, which works for all black box classifiers, but is only
an approximation and only works if the input dimension is not too high, whereas
our proposed method gives an exact measure.
