Modernizing Explainable AI Frameworks for Distributed Cloud Environments and Transparent Enterprise Decision Automation
DOI:
https://doi.org/10.15680/IJCTECE.2026.0904003Keywords:
Explainable Artificial Intelligence, Distributed Cloud Computing, Transparent Decision Automation, Machine Learning Interpretability, AI Governance, Federated Learning, Cloud-Native AI, Responsible Artificial Intelligence, Enterprise Automation, Model TransparencyAbstract
The rapid adoption of artificial intelligence (AI) within distributed cloud environments has transformed enterprise decision-making by enabling automated, scalable, and data-driven processes. However, the increasing complexity of AI models, particularly deep learning and hybrid machine learning systems, has created significant challenges regarding transparency, accountability, and trust. Modern enterprises require explainable artificial intelligence (XAI) frameworks that can operate effectively across decentralized cloud infrastructures while providing understandable insights into automated decisions. This essay explores the modernization of XAI frameworks designed for distributed cloud environments and their role in achieving transparent enterprise decision automation. The study examines emerging approaches such as federated explainability, interpretable machine learning, explainability-as-a-service, privacy-preserving explanation methods, and cloud-native AI governance mechanisms. It analyzes existing research challenges related to scalability, model complexity, data heterogeneity, regulatory compliance, and real-time explanation generation. A research methodology based on a mixed-method analytical approach is proposed, integrating systematic literature analysis, framework evaluation, architectural assessment, and comparative investigation of modern XAI techniques. The research emphasizes the importance of developing adaptive, secure, and context-aware explainability frameworks capable of supporting responsible AI adoption. Modernized XAI systems can enhance organizational trust, improve regulatory compliance, and enable transparent enterprise automation while maintaining the performance advantages of distributed cloud-based artificial intelligence
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