Explainable Artificial Intelligence via Causal Intervention Methods

Abstract

This paper proposes a novel approach to explainable artificial intelligence (XAI) by leveraging causal intervention methods. The core idea is to model systems using causal graphs and then employ techniques like do-calculus for precise intervention and control. Traditional intervention methods often lack interpretability, leading to uncertainties in control outcomes. This research addresses this limitation by integrating causality into XAI, aiming to enhance the accuracy and reliability of intervention strategies. The methodology focuses on understanding the 'what' and 'why' behind interventions through causal reasoning, offering a pathway to more trustworthy and explainable AI systems. The presented framework provides a robust mechanism for evaluating and refining intervention policies, ultimately contributing to a greater degree of transparency and control in AI-driven decision-making. This work emphasizes the importance of causal reasoning in building truly intelligent and accountable AI systems.