EXPLAINABLE AI FRAMEWORKS FOR ETHICAL DECISION-MAKING IN CLOUD-BASED AUTOMATION SYSTEMS

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.