Ethical and Interpretable AI Systems for Decision-Making in Autonomous Infrastructure Management

As artificial intelligence (AI) systems increasingly govern core infrastructure components, ethical and interpretable decision-making becomes essential to ensuring safety, compliance, and public trust. This paper introduces a unified framework that integrates ethical design principles and explainable AI (XAI) techniques into autonomous infrastructure systems. By embedding human oversight, fairness-aware reinforcement learning, and robust audit mechanisms, our approach enhances transparency in applications such as cloud resource management, cybersecurity enforcement, and load balancing. Real-world use cases and evaluations on a hybrid cloud testbed illustrate that these mechanisms improve fairness and compliance without significantly impacting system performance.