Designing Explainable AI Enabled Decision Systems for Hybrid Cloud Infrastructure and Intelligent Data Processing Frameworks
Abstract
The rapid evolution of hybrid cloud infrastructure and intelligent data processing frameworks has transformed the way organizations manage, analyze, and utilize large-scale digital resources. However, increasing complexity in distributed computing environments has created challenges related to transparency, trust, security, and effective decision-making. Explainable Artificial Intelligence (XAI) provides a promising approach for developing intelligent decision systems capable of delivering understandable, interpretable, and accountable outcomes. This research explores the design of explainable AI-enabled decision systems for hybrid cloud infrastructures by integrating machine learning techniques, data analytics frameworks, cloud orchestration mechanisms, and interpretability models. The study focuses on improving operational intelligence through automated resource management, predictive analytics, anomaly detection, workload optimization, and adaptive decision processes while ensuring that AI-generated recommendations can be understood by human stakeholders. A comprehensive methodological framework is proposed involving system architecture design, data collection, model development, explainability evaluation, and performance assessment. The research investigates how explainable models can enhance reliability, governance, and efficiency within complex cloud ecosystems. The proposed approach contributes to the development of trustworthy intelligent infrastructure by balancing automation with human-centered decision support. The findings are expected to support organizations in achieving scalable, transparent, and resilient hybrid cloud operations through responsible application of artificial intelligence technologies
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 10 |
| Number | 3 |
| Pages | 18883-18890 |
| Published | 2026-05-15 |
| Issue | Vol. 10 No. 3 (2026): International Research Journal of Innovative Engineering (IRJIE) |
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