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.