A Theoretical Probabilistic Framework for Explaining Generative AI

This study uses Generative Artificial Intelligence (gAI) to advance industrial digitization. Although the use of gAI looks promising for industrial digitization, there are significant gaps in current Explainable Artificial Intelligence (XAI) methods, which limit their applicability to such applications. By developing a theoretical framework, the aim is to provide explanations for gAI to improve decision-making processes with actionable insights and explanations for their intended outcomes. The proposed work has an impact on facilitating inspection, monitoring, optimization, and maintenance of industrial equipment and machinery. The theoretical framework proposed in this paper will address this challenge by following a three-step approach: 1) learning prior and posterior from data, 2) feature attribution and counterfactual explanation-based methods, and 3) integrated XAI. While the current study is theoretical, future work will focus on applying the approach to real-world industrial scenarios.