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

Chapter 8. An Ecosystem of Trust

So far in this book, you have learned about the tools required to embark on the journey of becoming an effective responsible ML practitioner. But are they enough? What else do you need to build, deploy, and iterate on ML systems that a diverse group of stakeholders can trust? Beyond the individual tools and techniques covered earlier, you need to know how to put these different pieces together to reach an ML-driven solution for your business problem while minimizing downstream risks. This sounds daunting at first, but there are resources in this growing field that can help achieve this goal.

This chapter will cover tools, tactics, and frameworks that provide a bird’s-eye view of what’s going on within and across ML models inside a company. To begin with, you’ll learn about technical tools for implementing ML pipelines and about guidelines and strategies to navigate the human-in-the-loop steps in ML pipelines effectively. You’ll be introduced to a few concepts and resources that help gain a cross-project outlook on ML workflows inside your company. Finally, you’ll see how all this knowledge can come together by exploring a deep-dive example of implementing an ML-based recommender system. If you are a product or engineering leader, the resources in this chapter will help you effectively collaborate with business stakeholders on implementing trusted ML. If you are an ML engineer or data scientist, you will gain valuable context for understanding trade-offs ...