Probabilistic Programming for Explainable AI – Causal Chain Exploration

Abstract

Explainable AI (XAI) is a critical area of research aimed at increasing the transparency and interpretability of complex AI models. Traditional XAI techniques often focus on post-hoc explanations, which can be misleading or lack a deep understanding of the underlying causal relationships. This paper proposes a novel methodology leveraging probabilistic programming to achieve truly explainable AI by explicitly modeling and exploring causal chains. The core idea is to represent a system's causal graph using probabilistic programming, allowing for systematic tracing of the pathways leading to a specific prediction. We quantify the associated probabilities at each step within these causal chains, providing a rigorous and transparent explanation. This approach moves beyond simple feature importance analysis, offering a deeper understanding of how factors contribute to a model's decision-making process. The methodology offers a robust framework for building XAI systems, particularly in domains where causal understanding is paramount.