Axiomatic Foundation for Explainable AI – Causal Inference as a Requirement

Abstract

Current approaches to explainable AI (XAI) frequently fall short of providing genuinely understandable and trustworthy explanations. These methods often rely on post-hoc interpretations of black-box models, which can be misleading due to their failure to capture the underlying causal mechanisms driving predictions. This paper proposes a novel axiomatic foundation for XAI, arguing that true explanation necessitates a thorough understanding of causal relationships. We posit that causal inference represents a fundamental requirement for explainability, moving beyond superficial feature importance or local linear approximations. This framework introduces a rigorous approach to assessing explanations by evaluating their consistency with known causal structures, leading to more robust and reliable explanations. We define key concepts and provide a formal outline for evaluating explanation methods based on their ability to accurately represent and leverage causal knowledge. The core contribution is establishing a clear criterion – causal fidelity – for evaluating XAI methods, ensuring explanations reflect the true causal drivers of model behavior.