Explainable AI via Counterfactual Explanations with Temporal Propagation

Abstract

The increasing deployment of Artificial Intelligence (AI) systems in critical applications necessitates methods for understanding and trusting their decisions. Traditional explainable AI (XAI) techniques often struggle to capture the complex, dynamic relationships within systems, particularly those evolving over time. This paper introduces a novel approach to generating counterfactual explanations that explicitly accounts for temporal dependencies. Our method leverages a temporal difference learning framework to model the impact of changes in input variables on the AI model's output, propagating these effects over time to generate more accurate and informative counterfactual scenarios. We demonstrate the efficacy of this approach through a theoretical framework, outlining the key components and mathematical formulations. The core contribution lies in the ability to provide explanations that move beyond static snapshots, reflecting the dynamic evolution of the AI system's behavior. This offers a significantly improved understanding of AI decision-making processes, crucial for building robust and trustworthy AI systems.