Explainable AI for Practitioners (Early Release, Ch1&2/8)

Description

Most intermediate-level machine learning books usually focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance and the need to be able to explain why and how your ML model makes the predictions that it does. This practical guide brings together the best-in-class techniques for model interpretability and explains model predictions in a hands-on approach. Experienced ML practitioners will be able to more easily apply these tools in their daily workflow. Foreword Preface Who Should Read This Book? What Is and What Is Not in This Book? Code Samples Navigating This Book Conventions Used in This Book O’Reilly Online Learning How to Contact Us Acknowledgments 1. Introduction Why Explainable AI What Is Explainable AI? Who Needs Explainability? Challenges in Explainability Evaluating Explainability How Has Explainability Been Used? How LinkedIn Uses Explainable AI PwC Uses Explainable AI for Auto Insurance Claims Accenture Labs Explains Loan Decisions DARPA Uses Explainable AI to Build “Third-Wave AI” Summary 2. An Overview of Explainability What Are Explanations? Interpretability and Explainability Explainability Consumers Practitioners—Data Scientists and ML Engineers Observers—Business Stakeholders and Regulators End Users—Domain Experts and Affected Users Types of Explanations Premodeling Explainability Intrinsic Versus Post Hoc Explainability Local, Cohort, and Global Explanations Attributions, Counterfactual, and Example-Based Explanations Themes Throughout Explainability Feature Attributions Surrogate Models Activation Putting It All Together Summary EARLY RELEASE ENDS HERE 3. Explainability for Tabular Data Permutation Feature Importance Permutation Feature Importance from Scratch Permutation Feature Importance in scikit-learn Shapley Values SHAP (SHapley Additive exPlanations) Visualizing Local Feature Attributions Visualizing Global Feature Attributions Interpreting Feature Attributions from Shapley Values Managed Shapley Values Explaining Tree-Based Models From Decision Trees to Tree Ensembles SHAP’s TreeExplainer Partial Dependence Plots and Related Plots Partial Dependence Plots (PDPs) Individual Conditional Expectation Plots (ICEs) Accumulated Local Effects (ALE) Summary 4. Explainability for Image Data Integrated Gradients (IG) Choosing a Baseline Accumulating Gradients Improvements on Integrated Gradients XRAI How XRAI Works Implementing XRAI Grad-CAM How Grad-CAM Works Implementing Grad-CAM Improving Grad-CAM LIME How LIME Works Implementing LIME Guided Backpropagation and Guided Grad-CAM Guided Backprop and DeConvNets Guided Grad-CAM Summary 5. Explainability for Text Data Overview of Building Models with Text Tokenization Word Embeddings and Pretrained Embeddings LIME How LIME Works with Text Gradient x Input Intuition from Linear Models From Linear to Nonlinear and Text Models Grad L2-norm Layer Integrated Gradients A Variation on Integrated Gradients Layer-Wise Relevance Propagation (LRP) How LRP Works Deriving Explanations from Attention Which Method to Use? Language Interpretability Tool Summary 6. Advanced and Emerging Topics Alternative Explainability Techniques Alternate Input Attribution Explainability by Design Other Modalities Time-Series Data Multimodal Data Evaluation of Explainability Techniques A Theoretical Approach Empirical Approaches Summary 7. Interacting with Explainable AI Who Uses Explainability? How to Effectively Present Explanations Clarify What, How, and Why the ML Performed the Way It Did Accurately Represent the Explanations Build on the ML Consumer’s Existing Understanding Common Pitfalls in Using Explainability Assuming Causality Overfitting Intent to a Model Overreaching for Additional Explanations Summary 8. Putting It All Together Building with Explainability in Mind The ML Life Cycle AI Regulations and Explainability What to Look Forward To in Explainable AI Natural and Semantic Explanations Interrogative Explanations Targeted Explanations Summary A. Taxonomy, Techniques, and Further Reading ML Consumers Taxonomy of Explainability XAI Techniques Tabular Models Image Models Text Models Advanced and Emerging Techniques Interacting with Explainability Putting It All Together Further Reading Explainable AI Interacting with Explainability Technical Accuracy of XAI techniques Brittleness of XAI techniques XAI for DNNs Index About the Authors