Machine Learning for High-Risk Applications Patrick Hall, James Curtis, Parul Pandey

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Spis treści książki

• Foreword
• Preface
  • Who Should Read This Book
  • What Readers Will Learn
  • Alignment with the NIST AI Risk Management Framework
  • Book Outline
    • Part I
    • Part II
    • Part III
  • Example Datasets
    • Taiwan Credit Data
    • Kaggle Chest X-Ray Data
  • Conventions Used in This Book
  • Online Figures
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • Patrick Hall
    • James Curtis
    • Parul Pandey
• 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
      • Known past failures
      • Failures of imagination
    • Model Risk Management Processes
      • Risk tiering
      • Model documentation
      • Model monitoring
      • Model inventories
      • System validation and process auditing
      • Change management
    • Beyond Model Risk Management
      • Model audits and assessments
      • Impact assessments
      • Appeal, override, and opt out
      • Pair and double programming
      • Security permissions for model deployment
      • Bug bounties
      • AI incident response
  • Case Study: The Rise and Fall of Zillows iBuying
    • Fallout
    • Lessons Learned
  • Resources
• 2. Interpretable and Explainable Machine Learning
  • Important Ideas for Interpretability and Explainability
  • Explainable Models
    • Additive Models
      • Penalized regression
      • Generalized additive models
      • GA2M and explainable boosting machines
    • Decision Trees
      • Single decision trees
      • Constrained XGBoost models
    • An Ecosystem of Explainable Machine Learning Models
  • Post Hoc Explanation
    • Feature Attribution and Importance
      • Local explanations and feature attribution
        • Shapley values
        • Critical applications of local explanations and feature importance
      • Global feature importance
    • Surrogate Models
      • Decision tree surrogates
      • Linear models and local interpretable model-agnostic explanations
      • Anchors and rules
    • Plots of Model Performance
      • Partial dependence and individual conditional expectation
      • Accumulated local effect
    • 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
      • Benchmarks and alternatives
      • Calibration
      • Construct validity
      • Assumptions and limitations
      • Default loss functions
      • Multiple comparisons
      • The future of safe and robust machine learning
  • Model Debugging
    • Software Testing
    • Traditional Model Assessment
    • Common Machine Learning Bugs
      • Distribution shifts
      • Epistemic uncertainty and data sparsity
      • Instability
      • Leakage
      • Looped inputs
      • Overfitting
      • Shortcut learning
      • Underfitting
      • Underspecification
    • Residual Analysis
      • Analysis and visualizations of residuals
      • Modeling residuals
      • Local contribution to residuals
    • Sensitivity Analysis
    • Benchmark Models
    • Remediation: Fixing Bugs
  • Deployment
    • Domain Safety
    • Model Monitoring
      • Model decay and concept drift
      • Detecting and addressing drift
      • Monitoring multiple key performance indicators
      • Out-of-range values
      • Anomaly detection and benchmark models
      • Kill switches
  • Case Study: Death by Autonomous Vehicle
    • Fallout
    • An Unprepared Legal System
    • Lessons Learned
  • Resources
• 4. Managing Bias in Machine Learning
  • ISO and NIST Definitions for Bias
    • Systemic Bias
    • Statistical Bias
    • Human Biases and Data Science Culture
  • Legal Notions of ML Bias in the United States
  • Who Tends to Experience Bias from ML Systems
  • Harms That People Experience
  • Testing for Bias
    • Testing Data
    • Traditional Approaches: Testing for Equivalent Outcomes
      • Statistical significance testing
      • Practical significance testing
    • A New Mindset: Testing for Equivalent Performance Quality
    • On the Horizon: Tests for the Broader ML Ecosystem
    • Summary Test Plan
  • Mitigating Bias
    • Technical Factors in Mitigating Bias
    • The Scientific Method and Experimental Design
    • Bias Mitigation Approaches
    • Human Factors in Mitigating Bias
  • Case Study: The Bias Bug Bounty
  • Resources
• 5. Security for Machine Learning
  • Security Basics
    • The Adversarial Mindset
    • CIA Triad
    • Best Practices for Data Scientists
  • Machine Learning Attacks
    • Integrity Attacks: Manipulated Machine Learning Outputs
      • Adversarial example attacks
      • Backdoor attacks
      • Data poisoning attacks
      • Impersonation and evasion attacks
      • Attacks on machine learning explanations
    • Confidentiality Attacks: Extracted Information
      • Model extraction and inversion attacks
      • Membership inference attacks
  • General ML Security Concerns
  • Countermeasures
    • Model Debugging for Security
      • Adversarial example searches and sensitivity analysis
      • Auditing for insider data poisoning
      • Bias testing
      • Ethical hacking: model extraction attacks
    • Model Monitoring for Security
    • Privacy-Enhancing Technologies
      • Federated learning
      • Differential privacy
    • Robust Machine Learning
    • General Countermeasures
  • Case Study: Real-World Evasion Attacks
    • Evasion Attacks
    • Lessons Learned
  • Resources
• II. Putting AI Risk Management into Action
• 6. Explainable Boosting Machines and Explaining XGBoost
  • Concept Refresher: Machine Learning Transparency
    • Additivity Versus Interactions
    • Steps Toward Causality with Constraints
    • Partial Dependence and Individual Conditional Expectation
    • Shapley Values
    • Model Documentation
  • The GAM Family of Explainable Models
    • Elastic NetPenalized GLM with Alpha and Lambda Search
    • Generalized Additive Models
    • GA2M and Explainable Boosting Machines
  • XGBoost with Constraints and Post Hoc Explanation
    • Constrained and Unconstrained XGBoost
    • Explaining Model Behavior with Partial Dependence and ICE
    • Decision Tree Surrogate Models as an Explanation Technique
    • Shapley Value Explanations
    • Problems with Shapley values
    • Better-Informed Model Selection
  • Resources
• 7. Explaining a PyTorch Image Classifier
  • Explaining Chest X-Ray Classification
  • Concept Refresher: Explainable Models and Post Hoc Explanation Techniques
    • Explainable Models Overview
    • Occlusion Methods
    • Gradient-Based Methods
    • Explainable AI for Model Debugging
  • Explainable Models
    • ProtoPNet and Variants
    • Other Explainable Deep Learning Models
  • Training and Explaining a PyTorch Image Classifier
    • Training Data
    • Addressing the Dataset Imbalance Problem
    • Data Augmentation and Image Cropping
    • Model Training
    • Evaluation and Metrics
    • Generating Post Hoc Explanations Using Captum
      • Occlusion
      • Input * gradient
      • Integrated gradients
      • Layer-wise Relevance Propagation
    • Evaluating Model Explanations
    • The Robustness of Post Hoc Explanations
  • Conclusion
  • Resources
• 8. Selecting and Debugging XGBoost Models
  • Concept Refresher: Debugging ML
    • Model Selection
    • Sensitivity Analysis
    • Residual Analysis
    • Remediation
  • Selecting a Better XGBoost Model
  • Sensitivity Analysis for XGBoost
    • Stress Testing XGBoost
    • Stress Testing Methodology
    • Altering Data to Simulate Recession Conditions
    • Adversarial Example Search
  • Residual Analysis for XGBoost
    • Analysis and Visualizations of Residuals
    • Segmented Error Analysis
    • Modeling Residuals
  • Remediating the Selected Model
    • Overemphasis of PAY_0
    • Miscellaneous Bugs
  • Conclusion
  • Resources
• 9. Debugging a PyTorch Image Classifier
  • Concept Refresher: Debugging Deep Learning
  • Debugging a PyTorch Image Classifier
    • Data Quality and Leaks
    • Software Testing for Deep Learning
    • Sensitivity Analysis for Deep Learning
      • Domain and subpopulation shift testing
      • Adversarial example attacks
      • Perturbing computational hyperparameters
    • Remediation
      • Data fixes
      • Software fixes
    • Sensitivity Fixes
      • Noise injection
      • Additional stability fixes
  • Conclusion
  • Resources
• 10. Testing and Remediating Bias with XGBoost
  • Concept Refresher: Managing ML Bias
  • Model Training
  • Evaluating Models for Bias
    • Testing Approaches for Groups
      • Testing performance
      • Traditional testing of outcomes rates
    • Individual Fairness
    • Proxy Bias
  • Remediating Bias
    • Preprocessing
    • In-processing
    • Postprocessing
    • Model Selection
  • Conclusion
  • Resources
• 11. Red-Teaming XGBoost
  • Concept Refresher
    • CIA Triad
    • Attacks
    • Countermeasures
  • Model Training
  • Attacks for Red-Teaming
    • Model Extraction Attacks
    • Adversarial Example Attacks
    • Membership Attacks
    • Data Poisoning
    • Backdoors
  • Conclusion
  • Resources
• III. Conclusion
• 12. How to Succeed in High-Risk Machine Learning
  • Who Is in the Room?
  • Science Versus Engineering
    • The Data-Scientific Method
    • The Scientific Method
  • Evaluation of Published Results and Claims
  • Apply External Standards
  • Commonsense Risk Mitigation
  • Conclusion
  • Resources
• Index

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