9798295533136 - Bias, Fairness & Discrimination in AI: How to Detect, Prevent & Correct Algorithmic Injustice: 2 (AI Ethics & Governance) de Correa, Joe (7 resultados)

Condición: New.

Paperback. Condición: new. Paperback. 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 scientistsBusiness executives and foundersCompliance officers and legal teamsGovernment and policy professionalsInvestors, regulators, and enterprise leadersInside 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 trustWhy 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. 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 scientistsBusiness executives and foundersCompliance officers and legal teamsGovernment and policy professionalsInvestors, regulators, and enterprise leadersInside 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 trustWhy 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condición: new. Paperback. 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 scientistsBusiness executives and foundersCompliance officers and legal teamsGovernment and policy professionalsInvestors, regulators, and enterprise leadersInside 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 trustWhy 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 scientistsBusiness executives and foundersCompliance officers and legal teamsGovernment and policy professionalsInvestors, regulators, and enterprise leadersInside 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 trustWhy 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.