In 2022, we investigated theoretical foundations of explainability, a hot topic in AI. In [1], we laid the foundations of explanation models by proposing key axioms, i.e., desirable properties they would satisfy, and characterized various families of models satisfying subsets of axioms. In [2], we analysed existing models against the axioms, highlighted their properties links and shortcomings. In [3], we proposed novel explanation models that satisfy the axioms while overcoming the shortcomings from the literature. Finally, we proposed in [4] a hybrid approach for ensemble classifiers. It combines machine learning models and nonmonotonic logics, namely argumentation-based logics. We show that the novel ensemble classifiers guarantee desirable properties including explainability, compliance to knowledge, and a global compatibility of the rules they use for making predictions. An experimental study conducted in the healthcare domain shows that the hybrid approach competes with existing ensemble methods.