Foreword Preface Part Ⅰ. Theories and Practical Applications of AI Risk Management 1.Contemporary Machine Learning Risk Management A Snapshot of the Legal and Regulatory Landscape The Proposed EU AI Act US Federal Laws and Regulations State and Municipal Laws Basic Product Liability Federal Trade Commission Enforcement Authoritative Best Practices AI Incidents Cultural Competencies for Machine Learning Risk Management Organizational Accountability Culture of Effective Challenge Diverse and Experienced Teams Drinking Our Own Champagne Moving Fast and Breaking Things Organizational Processes for Machine Learning Risk Management Forecasting Failure Modes Model Risk Management Processes Beyond Model Risk Management Case Study: The Rise and Fall of Zillow's iBuying ~ Fallout Lessons Learned Resources 2.Interpretable and Explainable Machine Learning Important Ideas for Interpretability and Explainability Explainable Models Additive Models Decision Trees An Ecosystem of Explainable Machine Learning Models Post Hoc Explanation Feature Attribution and Importance Surrogate Models Plots of Model Performance Cluster Profiling Stubborn Difficulties of Post Hoc Explanation in Practice Pairing Explainable Models and Post Hoc Explanation Case Study: Graded by Algorithm Resources 3.Debugging Machine Learning Systems for Safety and Performance Training Reproducibility Data Quality Model Specification for Real-World Outcomes Model Debugging Software Testing Traditional Model Assessment Common Machine Learning Bugs
Residual Analysis Sensitivity Analysis Benchmark Models Remediation: Fixing Bugs Deployment Domain Safety Model Monitoring Case Study: Death by Autonomous Vehicle Fallout An Unprepared Legal System Lessons Learned Resources …… 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
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