XXAI: Explicitly Explainable AI provides transparency in automatic decision-making by overcoming the limitations of symbolic AI

There are concerns about the reliability and safety of sub-symbolic neural network AI because its decisions cannot be explained explicitly. This is the black box problem of modern AI. At the same time, symbolic AI has the nature of a white box and is able to ensure the reliability and safety of its decisions. However, several problems prevent the widespread use of symbolic AI: the opacity of mathematical models and natural language terms, the lack of a unified ontology, and the combinatorial explosion of search capabilities. To solve the black-box problem of AI, we propose Explicitly Explainable AI (XXAI) - a fully transparent white-box AI based on deterministic logical cellular automata whose rules are derived from the first principles of the general theory of the relevant domain. In this case, the general theory of the domain plays the role of a knowledge base for deriving the inferences of the cellular automata. A cellular automaton implements parallel multi-level logical inference at all levels of organization - from local interactions of the element base to the system as a whole. Our verification of several ecological hypotheses sets a precedent for the successful implementation of the proposed solution. XXAI can automatically verify the reliability, safety, and ethicality of sub-symbolic neural network AI decisions during both the final and training phases. This paper presents the theoretical and methodological foundations for creating XXAI and discusses the prospects for this direction.