Explainable AI via Probabilistic Causal Graph Learning

Abstract

This paper explores a novel approach to developing explainable artificial intelligence (AI) by leveraging probabilistic causal graph learning. The core premise is that truly explainable AI requires the identification and representation of genuine causal relationships within data, translated into a readily interpretable probabilistic model. We introduce a mechanism that combines causal inference techniques with deep learning to automatically learn these causal structures. A critical element of this approach is the formalization of "causal uncertainty," providing a quantitative measure of confidence in the learned causal relationships. This framework offers a more robust and trustworthy method for generating explanations compared to traditional methods reliant on correlation alone. The proposed methodology addresses limitations in existing explainable AI techniques by grounding explanations in a verifiable causal structure. We demonstrate the potential of this approach through a theoretical framework, outlining the key components and considerations for successful implementation.