XXAI: Towards eXplicitly eXplainable Artificial Intelligence

TL;DR

This paper introduces XXAI, a fully transparent white-box AI based on cellular automata, which enhances explainability, reliability, and safety by deriving rules from domain theories and verifying neural network solutions.

Contribution

The paper proposes XXAI, a novel explainable AI framework using cellular automata derived from domain principles, addressing black-box issues in neural networks.

Findings

XXAI can verify neural network reliability and safety.

Successful implementation of ecological hypothesis verification.

Theoretical foundations for further development of transparent AI.

Abstract

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.