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Abstract
This research examines the integration of ethical considerations into AI DevOps practices. As AI systems proliferate in critical domains, traditional DevOps approaches require evolution to address unique ethical challenges throughout the system lifecycle. This research propose a comprehensive framework for Ethical AI DevOps that incorporates responsibility, transparency, fairness, and human-centricity as core principles in AI system development and deployment. The framework identifies ethical challenges in AI pipelines and presents best practices for each lifecycle stage, from planning to continuous assessment. Case studies demonstrate real-world application, revealing measurable improvements in bias detection, model transparency, and ethical governance. Findings emphasize the importance of organizational culture, automated safeguards, and continuous ethical monitoring. By bridging ethical principles with operational practices, this research provides actionable guidelines for practitioners developing AI systems in a responsible manner, contributing to the emerging field of ethical AI engineering.
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