Federated explainable artificial intelligence (fXAI): a digital manufacturing perspective

The industry has embraced digitalisation leading to a greater reliance on models derived from data. Understanding and getting insights into the models generated by machine learning algorithms is a challenge due to their non-explicit nature. Explainable artificial intelligence (XAI) is to enhance understanding of the digital models and confidence in the results they produce. The paper makes two contributions. First, the XRule algorithm proposed in the paper generates explicit rules meeting user's preferences. A user may control the nature of the rules generated by the XRule algorithm, e.g. degree of redundancy among the rules. Second, in analogy to federated learning, the concept of federated explainable artificial intelligence (fXAI) is proposed. Besides providing insights into the models built from data and explaining the predicted decisions, the fXAI provides additional value. The user-centric knowledge generated in support of fXAI may lead to discovery of previously unknown parameters and subsequently models that may benefit the non-explicit and explicit perspectives. The insights from fXAI could translate into new ways of modelling the phenomena of interest. A numerical example and three industrial applications illustrate the concepts presented in the paper.

Automated summary

The industry's shift towards digitalisation and reliance on data-derived models has led to the challenge of understanding and gaining insights from non-explicit machine learning algorithms. Explainable Artificial Intelligence (XAI) aims to enhance comprehension of digital models and confidence in their outcomes. This paper introduces the XRule algorithm for generating explicit rules based on user preferences and proposes the concept of Federated eXplainable Artificial Intelligence (fXAI). fXAI not only provides insights into data-built models and explains decision-making but also offers user-centric knowledge that can lead to new parameter discovery and improved modeling perspectives. The paper includes a numerical example and three industrial applications to illustrate these concepts.