Explainable AI for Practitioners: Designing and Implementing Explainable ML Solutions
- Length: 276 pages
- Edition: 1
- Language: English
- Publisher: O'Reilly Media
- Publication Date: 2022-12-06
- ISBN-10: 1098119134
- ISBN-13: 9781098119133
- Sales Rank: #863717 (See Top 100 Books)
ost intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does.
Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you’ll be able to apply these tools more easily in your daily workflow.
This essential book provides:
- A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needs
- Tips and best practices for implementing these techniques
- A guide to interacting with explainability and how to avoid common pitfalls
- The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems
- Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data
- Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace
Cover Copyright Table of Contents 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 Chapter 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 Chapter 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 Chapter 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 Chapter 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 Chapter 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 Chapter 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 Chapter 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 Chapter 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 Appendix 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 Colophon
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