Semantic Explanations of Classifiers through the Ontology-Based Data Management Paradigm (Extended Abstract)

One of the main challenges in modern AI systems is to explain the decisions of complex machine learning models, and recent years have seen a burgeoning of novel approaches. These approaches often rely on some structural components of the models under consideration, e.g., the set of features used for the classification task. As a result, explanations provided by these approaches are expressed in terms of the sub-symbolic information and, therefore, they are hard to interpret for users. In this paper, we argue that, in order to foster interpretability, these explanations should be expressed in terms of the knowledge that the users posses on the underlying application domain rather than on the sub-symbolic components of the model. To this end, our first contribution is the illustration of a novel formal framework for explaining the decisions of machine learning classifiers grounded on the Ontology-Based Data Management paradigm. Within this framework, explanations are defined by logical formulae using the symbols that an ontology defines and, as such, they posses a well-defined semantics. As a second contribution, we provide an algorithm that computes the best explanations that can be expressed in the class of conjunctive queries.