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Explainability as the key ingredient for AI adoption in Industry 5.0 settings.

Carlos Agostinho1,2, Zoumpolia Dikopoulou3, Eleni Lavasa4

  • 1Center of Technology and System (CTS), Instituto de Desenvolvimento de Novas Tecnologias (UNINOVA), Intelligent Systems Associate Laboratory (LASI), Caparica, Portugal.

Frontiers in Artificial Intelligence
|December 26, 2023
PubMed
Summary

Explainable Artificial Intelligence (XAI) enhances manufacturing by enabling transparent human-machine collaboration. The XMANAI platform builds interpretable AI models, boosting trust and operational efficiency in complex industrial settings.

Keywords:
Fuzzy Cognitive MapsXMANAI platformbusiness valuedecision-makingexplainable AImanufacturing industry

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Area of Science:

  • Artificial Intelligence
  • Manufacturing Technology
  • Human-Computer Interaction

Background:

  • Black box AI models lack transparency and interpretability, posing challenges in critical sectors like manufacturing.
  • The manufacturing industry requires trustworthy AI for complex decision-making and human-machine collaboration.
  • Existing AI solutions often fail to balance performance with the need for explainability.

Purpose of the Study:

  • Introduce the XMANAI platform, designed to foster transparent and trustworthy human-machine collaboration in manufacturing.
  • Address the
  • transparency paradox
  • in AI by enabling the construction of interpretable AI models without performance compromise.
  • Demonstrate the platform's capability to meet manufacturing-specific needs, including lifecycle management, security, and trusted AI asset sharing.

Main Methods:

  • Leveraging advancements in Explainable Artificial Intelligence (XAI).
  • Facilitating prompt collaboration between data scientists and domain experts.
  • Developing interpretable AI models with high transparency and performance.
  • Implementing functionalities for lifecycle management, security, and trusted sharing of AI assets.

Main Results:

  • The XMANAI platform enables the construction of interpretable AI models.
  • The platform successfully addresses technical challenges related to AI transparency.
  • An evaluation framework was developed to measure the performance of XAI solutions.
  • Demonstrated benefits include enhanced transparency in manufacturing decision-making.

Conclusions:

  • The XMANAI platform offers a viable solution to the AI transparency paradox in manufacturing.
  • It fosters trust and collaboration between humans and machines.
  • The approach improves operational efficiency and optimizes business value in the manufacturing sector.