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

✍ Scribed by Michael Munn; David Pitman

• Publisher: O'Reilly Media, Inc.
• Year: 2022
• Tongue: English
• Category: Library

✦ Synopsis

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.

✦ 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
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