With the increasing use of deep learning systems across various industries, there is a growing need to make their decision-making processes more understandable and transparent. Regulatory requirements now demand clarity, and users and stakeholders want to know how AI systems work. The textbook addresses these needs by providing a detailed guide on integrating Explainable AI (XAI) into the Deep Learning Operations (DLOps) pipeline. By doing so, organizations can implement Continuous Integration (CI) and Continuous Deployment (CD) practices effectively. Explainable AI: Building Trustworthy Deep Learning Systems focuses on how to incorporate XAI models, tools, and techniques to clarify machine learning decisions. It explores applications in fields such as healthcare, defense, human activity recognition, and object identification. The book offers practical advice on embedding XAI tools throughout the lifecycle of deep learning systems, covering topics like Explainability and Interpretability, Deep Learning Operations (DLOps), and Machine Learning Operations (MLOps). It also includes real-world examples, challenges, and solutions. This textbook is ideal for undergraduate and graduate students studying computer science, electronic and communications engineering, and electrical and electronics engineering. It is particularly suited for courses like AI Internals in Cyber-Physical Systems, AI Security Analytics, and Human-Computer Interaction with XAI. Professionals in systems engineering and industrial engineering will also find it valuable. For those adopting the textbook for courses, a solutions manual and PowerPoint slides are available.
