As artificial intelligence increasingly influences critical business and legal decisions, leaders can no longer accept "black box" models without explanation. Explainable AI for Non-Engineers provides executives, legal teams, and risk leaders with a clear and practical guide to understanding, demanding, and evaluating AI explanations.

This book explains why explainability matters for accountability, compliance, and trust—and how it differs across use cases such as credit decisions, healthcare, hiring, and automated risk scoring. It focuses on what non-technical decision-makers must know to govern AI responsibly.

Readers will learn:

• What explainable AI really means in practical terms

• Which explanations boards and regulators should expect

• How to assess explanations provided by AI vendors

• How explainability supports legal defensibility and audit readiness

• When explainability is mandatory versus optional

• How to embed explanation requirements into governance frameworks

Written in a clear and educational tone, this book equips non-engineers with the confidence to challenge opaque AI systems and demand explanations that support informed, defensible decisions.