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The application of artificial intelligence (AI) has grown significantly due to recent advances in machine learning (ML). However, deploying ML models within distributed computing environments introduces substantial challenges, particularly in terms of scalability and explainability. This review addresses the concept of scalable explainable AI (XAI), focusing on effective methodologies for deploying XAI across various distributed frameworks. The study aims to advance the development of transparent, reliable, and scalable AI applications, thereby ensuring greater trust and broader adoption in diverse operational environments.

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Scalable XAI: Towards Explainable Machine Learning Models in Distributed Systems

  • 会议论文

  • 发表日期：

    2025年

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  • Ibomoiye Domor Mienye,
  • Theo G. Swart,
  • George Obaido

摘要

The application of artificial intelligence (AI) has grown significantly due to recent advances in machine learning (ML). However, deploying ML models within distributed computing environments introduces substantial challenges, particularly in terms of scalability and explainability. This review addresses the concept of scalable explainable AI (XAI), focusing on effective methodologies for deploying XAI across various distributed frameworks. The study aims to advance the development of transparent, reliable, and scalable AI applications, thereby ensuring greater trust and broader adoption in diverse operational environments.