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. This 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 methods, 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 such as Explainability and Interpretability, Deep Learning Operations (DLOps), and Machine Learning Operations (MLOps). It also includes real-world examples, challenges, and solutions.
Researchers working in the area of Trustworthy AI, Responsible AI, and Explainable AI can use this book as a primary source as it contains code implementations with metrics calculations in detail. Moreover, professionals in IT indsutries applying AI in software development such as DevOps and Deep Learning architecture development with XAI integrations in systems engineering and industrial engineering will find it a highly valuable development guide.
For those adopting the textbook for courses, a solutions manual and PowerPoint slides are available.