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Foreword Preface Part I.Theories and Practical Applications of AI Risk Management 1.Contemporary Machine Learning Risk Management 2.Interpretable and Explainable Machine Learning 3.Debugging Machine Learning Systems for Safety and Performance 4.Managing Bias in Machine Learning 5.Security for Machine Learning Part II.Putting AI Risk Management into Action 6.Explainable Boosting Machines and Explaining XGBoost 7.Explaining a PyTorch Image Classifier 8.Selecting and Debugging XGBoost Models 9.Debugging a PyTorch Image Classifier 10.Testing and Remediating Bias with XGBoost 11.Red-Teaming XGBoost Part III.Conclusion 12.How to Succeed in High-Risk Machine Learning