Practicing Trustworthy Machine Learning Yada Pruksachatkun, Matthew Mcateer, Subho Majumdar

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Spis treści książki

• Preface
  • Implementing Machine Learning in Production
  • The Transformer Convergence
  • An Explosion of Large and Highly Capable ML Models
  • Why We Wrote This Book
  • Who This Book Is For
  • AI Safety and Alignment
  • Use of HuggingFace PyTorch for AI Models
  • Foundations
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Privacy
  • Attack Vectors for Machine Learning Pipelines
  • Improperly Implemented Privacy Features in ML: Case Studies
    • Case 1: Apples CSAM
    • Case 2: GitHub Copilot
    • Case 3: Model and Data Theft from No-Code ML Tools
  • Definitions
    • Definition of Privacy
    • Proxies and Metrics for Privacy
      • Adversarial success
      • Indistinguishability
      • Data similarity
      • Accuracy and precision
      • Uncertainty
      • Information gain/loss
      • Time spent
    • Legal Definitions of Privacy
    • k-Anonymity
  • Types of Privacy-Invading Attacks on ML Pipelines
    • Membership Attacks
    • Model Inversion
    • Model Extraction
  • Stealing a BERT-Based Language Model
    • Defenses Against Model Theft from Output Logits
    • Privacy-Testing Tools
  • Methods for Preserving Privacy
    • Differential Privacy
    • Stealing a Differentially Privately Trained Model
    • Further Differential Privacy Tooling
    • Homomorphic Encryption
    • Secure Multi-Party Computation
    • SMPC Example
    • Further SMPC Tooling
    • Federated Learning
  • Conclusion
• 2. Fairness and Bias
  • Case 1: Social Media
  • Case 2: Triaging Patients in Healthcare Systems
  • Case 3: Legal Systems
  • Key Concepts in Fairness and Fairness-Related Harms
    • Individual Fairness
    • Parity Fairness
    • Calculating Parity Fairness
      • Step 1: Dividing your test data into cohorts
      • Step 2: Get model performance results
      • Step 3: Evaluate for disparity
  • Scenario 1: Language Generation
  • Scenario 2: Image Captioning
  • Fairness Harm Mitigation
    • Mitigation Methods in the Pre-Processing Stage
    • Mitigation Methods in the In-Processing Stage
      • Adversarial bias mitigation
      • Regularization
    • Mitigation Methods in the Post-Processing Stage
  • Fairness Tool Kits
  • How Can You Prioritize Fairness in Your Organization?
  • Conclusion
  • Further Reading
• 3. Model Explainability and Interpretability
  • Explainability Versus Interpretability
  • The Need for Interpretable and Explainable Models
  • A Possible Trade-off Between Explainability and Privacy
  • Evaluating the Usefulness of Interpretation or Explanation Methods
  • Definitions and Categories
    • Black Box
    • Global Versus Local Interpretability
    • Model-Agnostic Versus Model-Specific Methods
    • Interpreting GPT-2
  • Methods for Explaining Models and Interpreting Outputs
    • Inherently Explainable Models
      • Linear regression
      • Logistic regression
      • Generalized linear model
      • Generalized additive models
      • Generalized additive models plus interactions
      • Symbolic regression
      • Support vector machines
      • Decision tree
      • Decision rules
      • Beyond intrinsically interpretable models
    • Local Model-Agnostic Interpretability Methods
      • Local interpretable model-agnostic explanation
      • Deep dive example: LIME on Vision Transformer models
      • Shapley and SHAP
      • Deep dive example: SHAP on Vision Transformer models
    • Global Model-Agnostic Interpretability Methods
      • Permutation feature importance
      • Global surrogate models
      • Prototypes and criticisms
    • Explaining Neural Networks
    • Saliency Mapping
    • Deep Dive: Saliency Mapping with CLIP
    • Adversarial Counterfactual Examples
  • Overcome the Limitations of Interpretability with a Security Mindset
  • Limitations and Pitfalls of Explainable and Interpretable Methods
  • Risks of Deceptive Interpretability
  • Conclusion
• 4. Robustness
  • Evaluating Robustness
  • Non-Adversarial Robustness
    • Step 1: Apply Perturbations
      • Computer vision
      • Language
    • Step 2: Defining and Applying Constraints
      • Natural language processing
        • Fluency
        • Preserving semantic meaning
      • Computer vision
    • Deep Dive: Word Substitution with Cosine Similarity Constraints
  • Adversarial Robustness
    • Deep Dive: Adversarial Attacks in Computer Vision
      • The HopSkipJump attack on ImageNet
    • Creating Adversarial Examples
  • Improving Robustness
  • Conclusion
• 5. Secure and Trustworthy Data Generation
  • Case 1: Unsecured AWS Buckets
  • Case 2: Clearview AI Scraping Photos from Social Media
  • Case 3: Improperly Stored Medical Data
  • Issues in Procuring Real-World Data
    • Using the Right Data for the Modeling Goal
    • Consent
    • PII, PHI, and Secrets
    • Proportionality and Sampling Techniques
    • Undescribed Variation
    • Unintended Proxies
    • Failures of External Validity
    • Data Integrity
    • Setting Reasonable Expectations
    • Tools for Addressing Data Collection Issues
      • Getting consent
      • Identifying PHI, PII, and other sensitive data
      • Proportionality and sampling techniques
      • Tracking unintended variation
      • Tracking unintended proxies
      • Data integrity
      • Improperly organized splits
      • Setting reasonable expectations
  • Synthetically Generated Data
    • DALLE, GPT-3, and Synthetic Data
    • Improving Pattern Recognition with Synthetic Data
      • Process-driven synthetic data
      • Data-driven synthetic data
    • Deep Dive: Pre-Training a Model with a Process-Driven Synthetic Dataset
    • Facial Recognition, Pose Detection, and Human-Centric Tasks
    • Object Recognition and Related Tasks
    • Environment Navigation
    • Unity and Unreal Environments
    • Limitations of Synthetic Data in Healthcare
    • Limitations of Synthetic Data in NLP
    • Self-Supervised Learned Models Versus Giant Natural Datasets
    • Repurposing Quality Control Metrics for Security Purposes
  • Conclusion
• 6. More State-of-the-Art Research Questions
  • Making Sense of Improperly Overhyped Research Claims
    • Shallow Human-AI Comparison Antipattern
    • Downplaying the Limitations of the Technique Antipattern
    • Uncritical PR Piece Antipattern
    • Hyperbolic or Just Plain Wrong Antipattern
    • Getting Past These Antipatterns
  • Quantized ML
    • Tooling for Quantized ML
    • Privacy, Bias, Interpretability, and Stability in Quantized ML
  • Diffusion-Based Energy Models
  • Homomorphic Encryption
  • Simulating Federated Learning
  • Quantum Machine Learning
    • Tooling and Resources for Quantum Machine Learning
    • Why QML Will Not Solve Your Regular ML Problems
  • Making the Leap from Theory to Practice
• 7. From Theory to Practice
  • Part I: Additional Technical Factors
    • Causal Machine Learning
      • Steps to causal inference
      • Tools for causal inference
      • Causality and trust
    • Sparsity and Model Compression
      • Pruning
      • Sparse training
      • Trust elements in sparse models
    • Uncertainty Quantification
      • Aleatoric uncertainty
      • Epistemic uncertainty
      • Confidence intervals
      • Bootstrap resampling
      • Are you certain I can trust you?
  • Part II: Implementation Challenges
    • Motivating Stakeholders to Develop Trustworthy ML Systems
      • Debt management
      • Risk management
    • Trust Debts
      • Technical trust debt
      • Ethical debt
    • Important Aspects of Trust
    • Evaluation and Feedback
    • Trustworthiness and MLOps
      • Scaling challenges
      • Data drift
      • Model monitoring and observability
      • Techniques
        • Anomaly detection
        • Change point detection
        • Control charts
  • Conclusion
• 8. An Ecosystem of Trust
  • Tooling
    • LiFT
    • Datasheets
    • Model Cards
    • DAG Cards
  • Human-in-the-Loop Steps
    • Oversight Guidelines
    • Stages of Assessment
      • Scoping
      • Data collection
      • Model training
      • Model validation
  • The Need for a Cross-Project Approach
    • MITRE ATLAS
    • Benchmarks
    • AI Incident Database
    • Bug Bounties
  • Deep Dive: Connecting the Dots
    • Data
    • Pre-Processing
    • Model Training
    • Model Inference
    • Trust Components
  • Conclusion
• A. Synthetic Data Generation Tools
• B. Other Interpretability and Explainability Tool Kits
  • Interpretable or Fair Modeling Packages
  • Other Python Packages for General Explainability
• Index

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