XAI for Industry 5.0—Concepts, Opportunities, Challenges, and Future Directions

Industry 5.0 has become a reality now and it is a paradigm that integrates contemporary innovations and concepts. Artificial Intelligence (AI) is a key component and asset of the industrial transformation which allows intelligent devices to perform functionalities such as self-examination, assessment, and evaluation autonomously. AI-based methodologies using ML and deep learning assist manufacturers and industrialists in forecasting service requirements and minimizing downtime. Recent research has discovered a remarkable change in the processes, systems, applications, and products in industries. Also, there is a significant challenge with the explainability of the decisions provided by the models using deep learning algorithms and their inadequate ability to be coupled with each other. Therefore, Explainable artificial intelligence (XAI) is required without compromising the efficiency of the models developed using deep learning algorithms. XAI investigates and develops algorithms, techniques, and models that produce human-comprehensible explanations of AI-based systems and can increase transparency and performance. The explainability nature of XAI will help humans understand the model and the reason behind the predictions, thus improving the model’s transparency and the reliability of the outcomes. Furthermore, an Industry 5.0-enabled environment has a variety of data from varied sources, and this multi-source information must be fused to derive meaningful and optimal decisions. Therefore, all AI-integrated applications must derive actionable insights through information fusion. Hence, the adoption of XAI methodologies in Industry 5.0 can help humans make trustworthy decisions for critical applications requiring information fusion. In this paper, we present a state-of-the-art survey on adopting XAI in Industry 5.0. We discuss the adoption of XAI in various applications such as smart factories, smart Healthcare, E-Governance, smart transportation, Education 5.0, Agriculture 5.0, and Energy 5.0. Finally, some research issues and future directions of integrating XAI with Industry 5.0 are also discussed and highlighted to promote more study in the potential field.