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 2. An Overview of Explainability

Explainability has been a part of machine learning since the inception of AI. The very first AIs, rule-based chain systems, were specifically constructed to provide a clear understanding of what led to a prediction. The field continued to pursue explainability as a key part of models, partly due to a focus on general AI but also to justify that the research was sane and on the right track, for many decades until the complexity of model architectures outpaced our ability to explain what was happening. After the introduction of ML neurons and neural nets in the 1980s,¹ research into explainability waned as researchers focused on surviving the first AI winter by turning to techniques that were “explainable” because they relied solely on statistical techniques, such as Bayesian inference, that were well-proven in other fields. Explainability in its modern form (and what we largely focus on in this book) was revived, now as a distinct field of research, in the mid-2010s in response to the persistent question of This model works really well…but how?

In just a few years, the field has gone from obscurity to one of intense interest and investigation. Remarkably, many powerful explainability techniques have been invented, or repurposed from other fields, in the short time since. However, the rapid transition from theory to practice, and the increasing need for explainability from users who interact with ML, such as end users and business stakeholders, ...