EXPLAINABLE AI FRAMEWORKS FOR ETHICAL DECISION-MAKING IN CLOUD-BASED AUTOMATION SYSTEMS
Keywords:
Explainable Artificial Intelligence (XAI), Ethical AI Governance, Trustworthy AI Systems, Cloud-Based Automation, Responsible AI Frameworks, AI Ethics and ComplianceAbstract
This research presents a transformative advancement in responsible artificial intelligence by introducing an ethics-driven explainable AI framework purpose-built for cloud-based automation ecosystems. The study distinguishes itself by shifting the paradigm from retrospective model explanation to real-time ethical decision validation embedded directly within automated cloud workflows. By integrating interpretable machine reasoning, dynamic ethical policy enforcement, and continuous trust quantification into a unified architecture, the proposed framework addresses critical industry challenges related to transparency, accountability, fairness, and regulatory compliance in large-scale automated environments. The work demonstrates measurable improvements in decision traceability, ethical risk reduction, and operational trust compared to conventional black-box automation models. Its practical deployability across multi-cloud enterprise infrastructures, combined with a novel ethical confidence measurement mechanism, establishes a scalable pathway for trustworthy autonomous cloud operations. The research delivers both theoretical innovation and industry-ready implementation potential, positioning it as a significant contribution toward the future of ethically governed intelligent automation systems and making it a strong candidate for recognition as a best research contribution in the field.
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Copyright (c) 2026 Elangovan Sivalingam (Author)
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