Unveiling the footprints of explainable AI in Industry 4.0/5.0: a systematic review and bibliometric exploration

Abstract

Progress in artificial intelligence (AI) is driving transformations that compel an increasing number of companies to embrace the Industry 4.0 and 5.0 paradigms and adopt advanced AI solutions. However, the lack of explainability in evaluating and justifying the decisions of AI models raises significant concerns regarding transparency, safety, and regulatory compliance, particularly in critical industrial applications. Hence, to understand the current explainable AI (XAI) approaches in the industry, this study performed a bibliometric analysis and systematic review concentrating on XAI within industrial research. In addition, the current trends address challenges and outline future directions. This study provides valuable insights that can aid researchers and practitioners in understanding the primary challenges involved in the effective application of XAI in Industry 4.0/5.0 research. Using the Scopus database, 82 articles published between 2019 and 2023 were analyzed, with an emphasis on the relevant outlets, institutions, countries, and keywords. Based on the review findings, there has been a continuous growth in XAI in industrial research. Future research should emphasize human-centric design, interdisciplinary collaboration, and continuous learning to enhance the adoption and effectiveness of XAI in Industry 4.0/5.0. This analysis shows that XAI in industry research is still in its infancy but tends to grow in the future. This review not only maps academic developments but also highlights the implications of XAI for industrial decision-making and managerial practice in smart manufacturing and related domains.