EXPLAINABLE AI Techniques that Meet Auditors' Needs

Artificial Intelligence is transforming business-but can your organization explain, defend, and audit its AI decisions?
As regulators, auditors, customers, and boards demand greater accountability, explainability has become a business imperative-not just a technical feature.
EXPLAINABLE AI: Techniques that Meet Auditors' Needs is a practical enterprise guide for building transparent, trustworthy, and audit-ready AI systems. Written for AI professionals, auditors, risk leaders, compliance teams, CISOs, technology executives, and governance professionals, this book bridges the gap between advanced AI technology and real-world accountability.
Inside, you'll learn how to:
Design explainable AI systems from the ground up
Implement SHAP, LIME, counterfactual explanations, and feature attribution techniques
Build audit trails, evidence repositories, and traceability frameworks
Prepare AI systems for internal audits, external reviews, and regulatory inspections
Align AI programs with the EU AI Act, NIST AI RMF, ISO/IEC 42001, GDPR, and emerging regulations
Govern foundation models, Generative AI, and autonomous AI systems responsibly
Establish continuous assurance and AI trust frameworks for the future
Packed with enterprise case studies, governance insights, audit perspectives, architecture patterns, and practical mini-projects, this book provides a roadmap for creating AI systems that stakeholders can understand, regulators can evaluate, and organizations can confidently defend.
If your AI must withstand scrutiny, this book belongs on your desk.

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