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Survey on ontology-based explainable AI in manufacturing

Author

Listed:
  • Muhammad Raza Naqvi

    (Laboratoire Génie de Production, Université de Technologie Tarbes Occitanie Pyrénées (UTTOP))

  • Linda Elmhadhbi

    (DISP EA4570)

  • Arkopaul Sarkar

    (Laboratoire Génie de Production, Université de Technologie Tarbes Occitanie Pyrénées (UTTOP))

  • Bernard Archimede

    (Laboratoire Génie de Production, Université de Technologie Tarbes Occitanie Pyrénées (UTTOP))

  • Mohamed Hedi Karray

    (Laboratoire Génie de Production, Université de Technologie Tarbes Occitanie Pyrénées (UTTOP))

Abstract

Artificial intelligence (AI) has become an essential tool for manufacturers seeking to optimize their production processes, reduce costs, and improve product quality. However, the complexity of the underlying mechanisms of AI systems can render it difficult for humans to understand and trust AI-driven decisions. Explainable AI (XAI) is a rapidly evolving field that addresses this challenge, providing human-understandable explanations of AI decisions. Based on a systematic literature survey, We explore the latest techniques and approaches that are helping manufacturers gain transparency in the decision-making processes of their AI systems. In this survey, we focus on two of the most exciting areas of XAI: ontology-based and semantic-based XAI (O-XAI, S-XAI, respectively), which provide human-readable explanations of AI decisions by exploiting semantic information. These latter types of explanations are presented in natural language and are designed to be easily understood by non-experts. Translating the decision paths taken by AI algorithms to meaningful explanations through semantics, O-XAI, and S-XAI enables humans to identify various cross-cutting concerns that influence the decisions made by the AI system. This information can be used to improve the performance of the AI system, identify potential biases in the system, and ensure that the decisions are aligned with the goals and values of the manufacturing organization. Additionally, we highlight the benefits and challenges of using O-XAI and S-XAI in manufacturing and discuss the potential for future research, aiming to provide valuable guidance for researchers and practitioners looking to leverage the power of ontologies and general semantics for XAI.

Suggested Citation

  • Muhammad Raza Naqvi & Linda Elmhadhbi & Arkopaul Sarkar & Bernard Archimede & Mohamed Hedi Karray, 2024. "Survey on ontology-based explainable AI in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3605-3627, December.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-023-02304-z
    DOI: 10.1007/s10845-023-02304-z
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    References listed on IDEAS

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    1. Eeva Järvenpää & Niko Siltala & Otto Hylli & Minna Lanz, 2019. "The development of an ontology for describing the capabilities of manufacturing resources," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 959-978, February.
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    Cited by:

    1. Joseph Cohen & Xun Huan & Jun Ni, 2024. "Shapley-based explainable AI for clustering applications in fault diagnosis and prognosis," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 4071-4086, December.
    2. Alexandre Dolgui & Hichem Haddou Benderbal & Fabio Sgarbossa & Simon Thevenin, 2024. "Editorial for the special issue: AI and data-driven decisions in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3599-3604, December.

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