"This book offers a critical view on the current practice of machine learning, as well as proposed technical fixes for achieving fairness in automated decisionmaking"--
"This book offers a critical view on the current practice of machine learning, as well as proposed technical fixes for achieving fairness in automated decisionmaking"--Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Preface ix Online Materials xiv Acknowledgments xv 1 Introduction 1 2 When Is Automated Decision Making Legitimate? 25 3 Classification 49 4 Relative Notions of Fairness 83 5 Causality 113 6 Understanding United States Antidiscrimination Law 151 7 Testing Discrimination in Practice 185 8 A Broader View of Discrimination 221 9 Datasets 251 References 285 Index 311
Preface ix Online Materials xiv Acknowledgments xv 1 Introduction 1 2 When Is Automated Decision Making Legitimate? 25 3 Classification 49 4 Relative Notions of Fairness 83 5 Causality 113 6 Understanding United States Antidiscrimination Law 151 7 Testing Discrimination in Practice 185 8 A Broader View of Discrimination 221 9 Datasets 251 References 285 Index 311
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