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Survey on ontology-based explainable AI in manufacturing

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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.

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Data availibility

The data related to Visualisation and Graphs generated during the current study are available from the corresponding author upon reasonable request.

Notes

  1. https://www.darpa.mil/attachments/XAIIndustryDay_Final.pptx.

  2. https://euagenda.eu/publications/industry-4-0.

  3. https://www.webofscience.com/WoS/WoScc/basic-search.

  4. https://www.mckinsey.com/featured-insights/artificial-intelligence/ai-adoption-advances-but-foundational-barriers-remain.

  5. https://insidebigdata.com/2022/01/21/2022-trends-in-semantic-technologies-humanizing-artificial-intelligence/

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Funding

This work is performed within the CHAIKMAT project funded by the French National Research Agency (ANR) under grant agreement “ANR-21-CE10-0004-01".

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Authors and Affiliations

  1. Laboratoire Génie de Production, Université de Technologie Tarbes Occitanie Pyrénées (UTTOP), 47 Av. d’Azereix, 65016, Tarbes, France

    Muhammad Raza Naqvi, Arkopaul Sarkar, Bernard Archimede & Mohamed Hedi Karray

  2. INSA Lyon, DISP EA4570, 69621, Lyon, France

    Linda Elmhadhbi

Authors
  1. Muhammad Raza Naqvi
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  2. Linda Elmhadhbi
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  3. Arkopaul Sarkar
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  4. Bernard Archimede
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  5. Mohamed Hedi Karray
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Contributions

MRN Conceptualized the research, curated the data, and wrote the primary manuscript. LE Reviewed, edited, and provided feedback on multiple versions of the manuscript. AS Played a significant role in literature review and validation. BA Assisted with software and visualization tools. MHK Reviewed, edited, and provided feedback on multiple versions of the manuscript. MRN, LE, AS, BA, MHK read and approved the final manuscript.

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Correspondence to Muhammad Raza Naqvi.

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Naqvi, M.R., Elmhadhbi, L., Sarkar, A. et al. Survey on ontology-based explainable AI in manufacturing. J Intell Manuf (2024). https://doi.org/10.1007/s10845-023-02304-z

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