  There are concerns about the reliability and safety of artificial
intelligence (AI) based on sub-symbolic neural networks 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 is able to
automatically verify the reliability, security and ethics of sub-symbolic
neural network solutions in both the final and training phases. In this
article, we present precedents for the successful implementation of XXAI, the
theoretical and methodological foundations for its further development, and
discuss prospects for the future.
