book

Explainable AI for Practitioners

by Michael Munn, David Pitman

October 2022

Beginner to intermediate

276 pages

8h 32m

English

O'Reilly Media, Inc.

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Foreword
Preface
Who Should Read This Book?What Is and What Is Not in This Book?Code SamplesNavigating This BookConventions Used in This BookO’Reilly Online LearningHow to Contact UsAcknowledgments
1. Introduction
Why Explainable AIWhat Is Explainable AI?Who Needs Explainability?Challenges in ExplainabilityEvaluating ExplainabilityHow Has Explainability Been Used?How LinkedIn Uses Explainable AIPwC Uses Explainable AI for Auto Insurance ClaimsAccenture Labs Explains Loan DecisionsDARPA Uses Explainable AI to Build “Third-Wave AI”Summary
2. An Overview of Explainability
What Are Explanations?Interpretability and ExplainabilityExplainability ConsumersPractitioners—Data Scientists and ML EngineersObservers—Business Stakeholders and RegulatorsEnd Users—Domain Experts and Affected UsersTypes of ExplanationsPremodeling ExplainabilityIntrinsic Versus Post Hoc ExplainabilityLocal, Cohort, and Global ExplanationsAttributions, Counterfactual, and Example-Based ExplanationsThemes Throughout ExplainabilityFeature AttributionsSurrogate ModelsActivationPutting It All TogetherSummary
3. Explainability for Tabular Data
Permutation Feature ImportancePermutation Feature Importance from ScratchPermutation Feature Importance in scikit-learnShapley ValuesSHAP (SHapley Additive exPlanations)Visualizing Local Feature AttributionsVisualizing Global Feature AttributionsInterpreting Feature Attributions from Shapley ValuesManaged Shapley ValuesExplaining Tree-Based ModelsFrom Decision Trees to Tree EnsemblesSHAP’s TreeExplainerPartial Dependence Plots and Related PlotsPartial Dependence Plots (PDPs)Individual Conditional Expectation Plots (ICEs)Accumulated Local Effects (ALE)Summary
4. Explainability for Image Data
Integrated Gradients (IG)Choosing a BaselineAccumulating GradientsImprovements on Integrated GradientsXRAIHow XRAI WorksImplementing XRAIGrad-CAMHow Grad-CAM WorksImplementing Grad-CAMImproving Grad-CAMLIMEHow LIME WorksImplementing LIMEGuided Backpropagation and Guided Grad-CAMGuided Backprop and DeConvNetsGuided Grad-CAMSummary
5. Explainability for Text Data
Overview of Building Models with TextTokenizationWord Embeddings and Pretrained EmbeddingsLIMEHow LIME Works with TextGradient x InputIntuition from Linear ModelsFrom Linear to Nonlinear and Text ModelsGrad L2-normLayer Integrated GradientsA Variation on Integrated GradientsLayer-Wise Relevance Propagation (LRP)How LRP WorksDeriving Explanations from AttentionWhich Method to Use?Language Interpretability ToolSummary
6. Advanced and Emerging Topics
Alternative Explainability TechniquesAlternate Input AttributionExplainability by DesignOther ModalitiesTime-Series DataMultimodal DataEvaluation of Explainability TechniquesA Theoretical ApproachEmpirical ApproachesSummary
7. Interacting with Explainable AI
Who Uses Explainability?How to Effectively Present ExplanationsClarify What, How, and Why the ML Performed the Way It DidAccurately Represent the ExplanationsBuild on the ML Consumer’s Existing UnderstandingCommon Pitfalls in Using ExplainabilityAssuming CausalityOverfitting Intent to a ModelOverreaching for Additional ExplanationsSummary
8. Putting It All Together
Building with Explainability in MindThe ML Life CycleAI Regulations and ExplainabilityWhat to Look Forward To in Explainable AINatural and Semantic ExplanationsInterrogative ExplanationsTargeted ExplanationsSummary

A. Taxonomy, Techniques, and Further Reading
ML ConsumersTaxonomy of ExplainabilityXAI TechniquesTabular ModelsImage ModelsText ModelsAdvanced and Emerging TechniquesInteracting with ExplainabilityPutting It All TogetherFurther ReadingExplainable AIInteracting with ExplainabilityTechnical Accuracy of XAI techniquesBrittleness of XAI techniquesXAI for DNNs
Index
About the Authors

Content preview from Explainable AI for Practitioners

About the Authors

Michael Munn is a research software engineer at Google in the ML Solutions team. His research focuses on better understanding the mathematical foundations of machine learning and how those insights can be used to improve machine learning models at Google. Previously, he worked in the Google Cloud Advanced Solutions Lab helping customers design, implement, and deploy machine learning models at scale. Michael has a PhD in mathematics from the City University of New York. Before joining Google, he worked as a research professor.

David Pitman is a staff engineer working in Google Cloud on the AI platform, where he leads the Explainable AI team. He is also a co-organizer of PuPPy, the largest Python group in the Pacific Northwest. David has a MEng and a BS in computer science, focusing on AI and human-computer interaction, from MIT, where he was previously a research scientist.

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Publisher Resources

ISBN: 9781098119126Errata Page

Cloud Computing

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Explainable AI for Practitioners

by Michael Munn, David Pitman

About the Authors

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.