book

Practicing Trustworthy Machine Learning

by Yada Pruksachatkun, Matthew Mcateer, Subho Majumdar

January 2023

Intermediate to advanced

300 pages

8h 8m

English

O'Reilly Media, Inc.

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Implementing Machine Learning in ProductionThe Transformer ConvergenceAn Explosion of Large and Highly Capable ML ModelsWhy We Wrote This BookWho This Book Is ForAI Safety and AlignmentUse of HuggingFace PyTorch for AI ModelsFoundationsConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
Attack Vectors for Machine Learning PipelinesImproperly Implemented Privacy Features in ML: Case StudiesCase 1: Apple’s CSAMCase 2: GitHub CopilotCase 3: Model and Data Theft from No-Code ML ToolsDefinitionsDefinition of PrivacyProxies and Metrics for PrivacyLegal Definitions of Privacyk-AnonymityTypes of Privacy-Invading Attacks on ML PipelinesMembership AttacksModel InversionModel ExtractionStealing a BERT-Based Language ModelDefenses Against Model Theft from Output LogitsPrivacy-Testing ToolsMethods for Preserving PrivacyDifferential PrivacyStealing a Differentially Privately Trained ModelFurther Differential Privacy ToolingHomomorphic EncryptionSecure Multi-Party ComputationSMPC ExampleFurther SMPC ToolingFederated LearningConclusion
Case 1: Social MediaCase 2: Triaging Patients in Healthcare SystemsCase 3: Legal SystemsKey Concepts in Fairness and Fairness-Related HarmsIndividual FairnessParity FairnessCalculating Parity FairnessScenario 1: Language GenerationScenario 2: Image CaptioningFairness Harm MitigationMitigation Methods in the Pre-Processing StageMitigation Methods in the In-Processing StageMitigation Methods in the Post-Processing StageFairness Tool KitsHow Can You Prioritize Fairness in Your Organization?ConclusionFurther Reading
Explainability Versus InterpretabilityThe Need for Interpretable and Explainable ModelsA Possible Trade-off Between Explainability and PrivacyEvaluating the Usefulness of Interpretation or Explanation MethodsDefinitions and Categories“Black Box”Global Versus Local InterpretabilityModel-Agnostic Versus Model-Specific MethodsInterpreting GPT-2Methods for Explaining Models and Interpreting OutputsInherently Explainable ModelsLocal Model-Agnostic Interpretability MethodsGlobal Model-Agnostic Interpretability MethodsExplaining Neural NetworksSaliency MappingDeep Dive: Saliency Mapping with CLIPAdversarial Counterfactual ExamplesOvercome the Limitations of Interpretability with a Security MindsetLimitations and Pitfalls of Explainable and Interpretable MethodsRisks of Deceptive InterpretabilityConclusion
Evaluating RobustnessNon-Adversarial RobustnessStep 1: Apply PerturbationsStep 2: Defining and Applying ConstraintsDeep Dive: Word Substitution with Cosine Similarity ConstraintsAdversarial RobustnessDeep Dive: Adversarial Attacks in Computer VisionCreating Adversarial ExamplesImproving RobustnessConclusion
Case 1: Unsecured AWS BucketsCase 2: Clearview AI Scraping Photos from Social MediaCase 3: Improperly Stored Medical DataIssues in Procuring Real-World DataUsing the Right Data for the Modeling GoalConsentPII, PHI, and SecretsProportionality and Sampling TechniquesUndescribed VariationUnintended ProxiesFailures of External ValidityData IntegritySetting Reasonable ExpectationsTools for Addressing Data Collection IssuesSynthetically Generated DataDALL·E, GPT-3, and Synthetic DataImproving Pattern Recognition with Synthetic DataDeep Dive: Pre-Training a Model with a Process-Driven Synthetic DatasetFacial Recognition, Pose Detection, and Human-Centric TasksObject Recognition and Related TasksEnvironment NavigationUnity and Unreal EnvironmentsLimitations of Synthetic Data in HealthcareLimitations of Synthetic Data in NLPSelf-Supervised Learned Models Versus Giant Natural DatasetsRepurposing Quality Control Metrics for Security PurposesConclusion
Making Sense of Improperly Overhyped Research ClaimsShallow Human-AI Comparison AntipatternDownplaying the Limitations of the Technique AntipatternUncritical PR Piece AntipatternHyperbolic or Just Plain Wrong AntipatternGetting Past These AntipatternsQuantized MLTooling for Quantized MLPrivacy, Bias, Interpretability, and Stability in Quantized MLDiffusion-Based Energy ModelsHomomorphic EncryptionSimulating Federated LearningQuantum Machine LearningTooling and Resources for Quantum Machine LearningWhy QML Will Not Solve Your Regular ML ProblemsMaking the Leap from Theory to Practice
Part I: Additional Technical FactorsCausal Machine LearningSparsity and Model CompressionUncertainty QuantificationPart II: Implementation ChallengesMotivating Stakeholders to Develop Trustworthy ML SystemsTrust DebtsImportant Aspects of TrustEvaluation and FeedbackTrustworthiness and MLOpsConclusion
ToolingLiFTDatasheetsModel CardsDAG CardsHuman-in-the-Loop StepsOversight GuidelinesStages of AssessmentThe Need for a Cross-Project ApproachMITRE ATLASBenchmarksAI Incident DatabaseBug BountiesDeep Dive: Connecting the DotsDataPre-ProcessingModel TrainingModel InferenceTrust ComponentsConclusion

Interpretable or Fair Modeling PackagesOther Python Packages for General Explainability

Content preview from Practicing Trustworthy Machine Learning

Preface

We live in a world where machine learning (ML) systems are used in increasingly high-stakes domains like medicine, law, and defense. Model decisions can result in economic gains or losses in the millions or billions of dollars. Because of the high-stakes nature of their decisions and consequences, it is important for these ML systems to be trustworthy. This can be a problem when the ML systems are not secure, may fail unpredictably, have notable performance disparities across sample groups, and/or struggle to explain their decisions. We wrote this book to help your ML models stand up on their own in the real world.

Implementing Machine Learning in Production

If you’re reading this book, you are probably already aware of the incredibly outsized importance of ML. Regardless of the fields of application, ML techniques touch all of our lives. Google Brain cofounder Andrew Ng was not exaggerating when he described AI as “the new electricity”. After all, what we have on our hands could best be described as a universal function approximator. Much like electricity, ML can be dangerous if not handled properly. Like a discharge from a high-voltage wire colliding with a mylar balloon, cases of ML failure can be unexpected and scary.

Deploying ML applications in the real world is quite different from working on models in closed environments. Academic datasets often do not carry the full variation of real-world data. Data that our models interact with in the future may not resemble ...