Overview

Bias, Fairness & Discrimination in AI is a definitive professional guide to one of the most dangerous and misunderstood risks in artificial intelligence: algorithmic injustice. As AI increasingly controls hiring, lending, healthcare, law enforcement, and government services, biased systems can silently deny opportunity, misidentify individuals, and automate discrimination at massive scale. This book reveals how bias enters AI, how it hides inside data and models, and how organizations can detect, prevent, and correct it before it becomes a legal, ethical, and financial disaster. This book is written for: AI developers and data scientists Business executives and founders Compliance officers and legal teams Government and policy professionals Investors, regulators, and enterprise leaders Inside this book you will learn: - Where algorithmic bias really comes from - How training data silently embeds inequality - Why facial recognition and hiring AI fail - How credit and healthcare algorithms discriminate - What legal liability exists for biased AI - How to perform professional bias audits - How fairness metrics actually work - How to design ethical, defensible AI systems - How to build AI that earns public trust Why this book matters: Biased AI is already triggering lawsuits, regulatory crackdowns, and public backlash worldwide. This book gives you the tools to stay compliant, avoid reputational damage, and build systems that survive the future of regulation. If you are building, deploying, regulating, or investing in AI, this book is no longer optional. It is required reading for the next decade of technology leadership.

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