(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The growing deployment of artificial intelligence in high-stakes domains such as healthcare, criminal justice, and credit scoring has exposed a critical need for systems that deliver decisions not only with high accuracy but also with interpretability and accountability. Engineering explainable AI systems for transparent and trustworthy decision-making requires a holistic perspective that moves beyond isolated algorithmic explanation modules to encompass system-level architecture, infrastructure, governance, and policy integration. This paper presents an interdisciplinary analysis of the design principles, structural trade-offs, and sustainability challenges inherent in building explainable AI into large-scale sociotechnical systems. We examine the layered architecture of explainability, from model-specific post-hoc interpretation to institution-wide transparency mechanisms, and discuss how modular explanation components can be embedded within end-to-end machine learning pipelines without compromising performance or maintainability. The discussion extends to deployment infrastructure, contrasting cloud-native and edge-based explanation serving, and explores the governance frameworks that align technical explainability with legal concepts such as contestability and the right to meaningful information. Robustness of explanations against adversarial manipulation, fairness audits over time, and the carbon footprint of explanation generation are analyzed as dimensions of sustainable system design. By integrating insights from machine learning, human-computer interaction, regulatory studies, and systems engineering, the paper argues that trustworthy AI is not solely a model property but an emergent feature of a carefully engineered socio-technical infrastructure. The analysis culminates in a set of architectural guidelines and open challenges that inform future research and policy-making for transparent AI systems.