For the second aspect, a promising route is the use of subgroup discovery for the identification of the socalled domains of applicability (DAs, regions of the input space where a predictive model yields small errors) , which are given in form of descriptive rules, i.e., inequalities over a set of features, identified among a larger set of candidates. Although it has been shown that DAs can be found and the descriptive rules give insight on the analyzed ML models, the method has not been yet further developed to systematically identify outliers and exploited to improve the underlying ML model, e.g., in an AL fashion.