Probabilistic Programming for Explainable AI – Causal Chain Analysis

Abstract

Explainable AI (XAI) has emerged as a critical area of research, driven by the need for trust and accountability in increasingly complex AI systems. However, many existing XAI techniques offer only superficial explanations, often failing to capture the underlying causal mechanisms driving AI decisions. This paper proposes a novel approach leveraging probabilistic programming languages to provide truly explainable AI through causal chain analysis. We argue that current XAI methods frequently lack grounding in causal relationships, generating explanations that are ultimately misleading. Our core mechanism involves modeling AI decision-making processes as causal chains within a probabilistic programming environment, such as Stan or PyMC3. This allows us to automatically generate and analyze these chains, quantifying the influence of each input variable on the predicted output while identifying potential biases. The resulting framework offers a rigorous and transparent method for understanding *why* an AI system made a specific decision, moving beyond correlation to establish cause-and-effect relationships. The core claim is that this approach provides a significantly more robust and reliable basis for XAI than existing techniques. We will demonstrate how this framework can be used to identify and mitigate biases, leading to more trustworthy and reliable AI systems.