Explainable AI for Industry 5.0: Vision, Architecture, and Potential Directions

Chandan Trivedi, Pronaya Bhattacharya, Vivek Kumar Prasad, Viraj Patel, Arunendra Singh, Sudeep Tanwar, Ravi Sharma, Srinivas Aluvala, Giovanni Pau, Gulshan Sharma

Research output: Contribution to journalArticlepeer-review


The Industrial Revolution has shifted toward Industry 5.0, reinventing the Industry 4.0 operational process by introducing human elements into critical decision processes. Industry 5.0 would present massive customization via transformative technologies, such as cyber-physical systems (CPSs), artificial intelligence (AI), and big data analytics. In Industry 5.0, the AI models must be transparent, valid, and interpretable. AI models employ machine learning and deep learning mechanisms to make the industrial process autonomous, reduce downtime, and improve operational and maintenance costs. However, the models require explainability in the learning process. Thus, explainable AI (EXAI) adds interpretability and improves the diagnosis of critical industrial processes, which augments the machine-to-human explanations and vice versa. Recent surveys of EXAI in industrial applications are mostly oriented toward EXAI models, the underlying assumptions. Still, fewer studies are conducted toward a holistic integration of EXAI with human-centric processes that drives the Industry 5.0 applicative verticals. Thus, to address the gap, we propose a first-of-its-kind survey that systematically untangles EXAI integration and its potential in Industry 5.0 applications. First, we present the background of EXAI in Industry 5.0 and CPSs and a reference EXAI-based Industry 5.0 architecture with insights into large language models. Then, based on the research questions, a solution taxonomy of EXAI in Industry 5.0 is presented, which is ably supported by applicative use cases (cloud, digital twins, smart grids, augmented reality, and unmanned aerial vehicles). Finally, a case study of EXAI in manufacturing cost assessment is discussed, followed by open issues and future directions. The survey is designed to extend novel prototypes and designs to realize EXAI-based real-time Industry 5.0 applications.

Original languageEnglish
Pages (from-to)177-208
Number of pages32
JournalIEEE Open Journal of Industry Applications
Publication statusPublished - 2024


  • Automation
  • Industry 5.0
  • cobots
  • cyber-physical systems (CPSs)
  • digital twins (DTs)
  • explainable artificial intelligence (EXAI)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


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