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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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
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
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
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
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
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
Alternative Explainability TechniquesAlternate Input AttributionExplainability by DesignOther ModalitiesTime-Series DataMultimodal DataEvaluation of Explainability TechniquesA Theoretical ApproachEmpirical ApproachesSummary
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
Building with Explainability in MindThe ML Life CycleAI Regulations and ExplainabilityWhat to Look Forward To in Explainable AINatural and Semantic ExplanationsInterrogative ExplanationsTargeted ExplanationsSummary

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

Content preview from Explainable AI for Practitioners

Chapter 8. Putting It All Together

In this book, we have given you, the ML practitioner, a framework for how to use Explainable AI (XAI) and where it can be best applied. We also gave you a toolbox of explainable techniques to apply in different scenarios, and guides for crafting responsible and beneficial interactions with explanations. In this chapter, we step back to focus on the bigger picture around Explainable AI. With the tools and capabilities that we covered in this book, how can you approach the entire ML workflow and build with explainability in mind? We also provide a preview of the upcoming AI regulations and standards that will require explainability.

Building with Explainability in Mind

Many times, explainability is approached as an afterthought to model development, an added bonus to developing your most recent top-performing model or a post hoc feature request required by your boss trying to adhere to some new regulatory constraint that’s been imposed on the business. However, explainability and the goal of XAI is much more than that.

Throughout this book, we’ve discussed in detail a number of explainability techniques and seen how they can be applied for tabular, image, and text data. For the most part, we’ve explored these techniques in isolation so you, the reader, can quickly get up to speed on commonly used methods, how they work, their pros, and their cons. Of course, in practice explainability doesn’t occur in isolation. You should consider these techniques ...