Artificial Intelligence is rapidly transforming healthcare, finance, manufacturing, education, legal services, software engineering, media, and government. As AI systems become increasingly capable, a critical question emerges:
Can society trust decisions made by intelligent machines?
Traditional Explainable AI (XAI) focuses on understanding why a model produced a particular result. While transparency remains important, modern organizations require much more. They must verify intent, validate evidence, assess outcomes, enforce policies, satisfy regulations, and maintain accountability across complex AI-driven operations.
This book introduces Industrial Explainable AI (Industrial XAI)-a new framework that extends explainability beyond model interpretation into the broader domains of governance, trust, and operational accountability.
Inspired by recent advances in explainable AI and interpretability research, including the bridge-building work of Dr. Tessa Han and her colleagues, this book explores how explainability can evolve into a practical infrastructure for trustworthy AI deployment.
Inside, you will discover:
- The evolution from traditional explainability to Industrial XAI
- The Agentic Interpretability Protocol (AIP) and the Intent-Proof-Value framework
- Governance Ledgers for recording and auditing AI decisions
- Global Interpretability Clearinghouses for cross-organizational trust verification
- AI Governors for managing AI-enabled operational workflows
- Adaptive governance architectures for healthcare, finance, manufacturing, legal systems, education, media, software engineering, and government
- The emerging concept of the Safety Logic Unit (SLU), a potential hardware foundation for AI governance
- The future of explainability in the era of Physical AI, AGI, and beyond
Rather than treating explainability as a collection of dashboards and reports, Industrial XAI views explainability as a foundational layer for building governable trust.
The book argues that the next major opportunity in AI may not lie solely in larger models or faster computation, but in the creation of trustworthy governance infrastructures capable of supporting increasingly intelligent systems. As AI becomes more deeply embedded in critical decision-making, organizations will require mechanisms to reconcile intent, proof, value, compliance, and accountability at scale.
Industrial Explainable AI presents a practical and forward-looking vision for researchers, engineers, architects, executives, policymakers, investors, and industry leaders seeking to understand the next stage of trustworthy AI.
Whether you are building AI systems, governing them, regulating them, or investing in them, this book provides a framework for thinking about one of the most important challenges of the AI era:
How do we build intelligent systems that society can trust?
Tang, Charles: - Charles Tang is a software technologist, entrepreneur, inventor, mentor, and researcher whose career spans software engineering, enterprise systems, cloud computing, artificial intelligence, and emerging technologies.A graduate of National Taiwan University and the Massachusetts Institute of Technology (MIT), he has held technical and leadership positions with major technology organizations and founded software companies supporting mission-critical government and industry projects. Throughout his career, he has been awarded numerous patents covering software systems, cloud computing, and advanced computing technologies.In recent years, his research interests have focused on Explainable AI (XAI), AI governance, agentic systems, quantum computing, and trustworthy intelligent infrastructures. He is the founder of the Quantum Intelligence Association (QIA) and has actively mentored professionals and students across multiple industries, helping them navigate the opportunities and challenges created by the AI revolution.Drawing upon decades of experience in technology, systems architecture, and organizational leadership, he developed the concepts of Industrial XAI, the Agentic Interpretability Protocol (AIP), Governance Ledgers, Global Interpretability Clearinghouses, AI Governors, and Safety Logic Units (SLUs) as frameworks for building governable trust in increasingly intelligent systems.Through Industrial XAI, Charles presents a vision for the future in which explainability evolves beyond model interpretation into a comprehensive trust infrastructure capable of supporting the next generation of Physical AI, AGI, and beyond.
