کتاب یادگیری ماشین برای کاربردهای پرخطر

مدیریت ریسک یادگیری ماشین معاصر

A Snapshot of the Legal and Regulatory Landscape • The Proposed EU AI Act • US Federal Laws and Regulations • State and Municipal Laws • Basic Product Liability • Federal Trade Commission Enforcement • Authoritative Best Practices • AI Incidents • Cultural Competencies for Machine Learning Risk Management • Organizational Accountability • Culture of Effective Challenge • Diverse and Experienced Teams • Drinking Our Own Champagne • Moving Fast and Breaking Things • Organizational Processes for Machine Learning Risk Management • Forecasting Failure Modes • Model Risk Management Processes • Beyond Model Risk Management • Case Study: The Rise and Fall of Zillow’s iBuying • Fallout • Lessons Learned • Resources

یادگیری ماشین تفسیرپذیر و توضیح‌پذیر

Important Ideas for Interpretability and Explainability • Explainable Models • Additive Models • Decision Trees • An Ecosystem of Explainable Machine Learning Models • Post Hoc Explanation • Feature Attribution and Importance • Surrogate Models • Plots of Model Performance • Cluster Profiling • Stubborn Difficulties of Post Hoc Explanation in Practice • Pairing Explainable Models and Post Hoc Explanation • Case Study: Graded by Algorithm • Resources

دیباگ کردن سیستم‌های یادگیری ماشین برای ایمنی و عملکرد

Training • Reproducibility • Data Quality • Model Specification for Real-World Outcomes • Model Debugging • Software Testing • Traditional Model Assessment • Common Machine Learning Bugs • Residual Analysis • Sensitivity Analysis • Benchmark Models • Remediation: Fixing Bugs • Deployment • Domain Safety • Model Monitoring • Case Study: Death by Autonomous Vehicle • Fallout • An Unprepared Legal System • Lessons Learned • Resources

مدیریت سوگیری در یادگیری ماشین

ISO and NIST Definitions for Bias • Systemic Bias • Statistical Bias • Human Biases and Data Science Culture • Legal Notions of ML Bias in the United States • Who Tends to Experience Bias from ML Systems • Harms That People Experience • Testing for Bias • Testing Data • Traditional Approaches: Testing for Equivalent Outcomes • A New Mindset: Testing for Equivalent Performance Quality • On the Horizon: Tests for the Broader ML Ecosystem • Summary Test Plan • Mitigating Bias • Technical Factors in Mitigating Bias • The Scientific Method and Experimental Design • Bias Mitigation Approaches • Human Factors in Mitigating Bias • Case Study: The Bias Bug Bounty • Resources

امنیت برای یادگیری ماشین

Security Basics • The Adversarial Mindset • CIA Triad • Best Practices for Data Scientists • Machine Learning Attacks • Integrity Attacks: Manipulated Machine Learning Outputs • Confidentiality Attacks: Extracted Information • General ML Security Concerns • Countermeasures • Model Debugging for Security • Model Monitoring for Security • Privacy-Enhancing Technologies • Robust Machine Learning • General Countermeasures • Case Study: Real-World Evasion Attacks • Evasion Attacks • Lessons Learned • Resources

ماشین‌های تقویت‌کننده توضیح‌پذیر و توضیح XGBoost

Concept Refresher: Machine Learning Transparency • Additivity Versus Interactions • Steps Toward Causality with Constraints • Partial Dependence and Individual Conditional Expectation • Shapley Values • Model Documentation • The GAM Family of Explainable Models • Elastic Net–Penalized GLM with Alpha and Lambda Search • Generalized Additive Models • GA2M and Explainable Boosting Machines • XGBoost with Constraints and Post Hoc Explanation • Constrained and Unconstrained XGBoost • Explaining Model Behavior with Partial Dependence and ICE • Decision Tree Surrogate Models as an Explanation Technique • Shapley Value Explanations • Problems with Shapley values • Better-Informed Model Selection • Resources

توضیح یک طبقه‌بند تصویر PyTorch

Explaining Chest X-Ray Classification • Concept Refresher: Explainable Models and Post Hoc Explanation Techniques • Explainable Models Overview • Occlusion Methods • Gradient-Based Methods • Explainable AI for Model Debugging • Explainable Models • ProtoPNet and Variants • Other Explainable Deep Learning Models • Training and Explaining a PyTorch Image Classifier • Training Data • Addressing the Dataset Imbalance Problem • Data Augmentation and Image Cropping • Model Training • Evaluation and Metrics • Generating Post Hoc Explanations Using Captum • Evaluating Model Explanations • The Robustness of Post Hoc Explanations • Conclusion • Resources

انتخاب و دیباگ کردن مدل‌های XGBoost

Concept Refresher: Debugging ML • Model Selection • Sensitivity Analysis • Residual Analysis • Remediation • Selecting a Better XGBoost Model • Sensitivity Analysis for XGBoost • Stress Testing XGBoost • Stress Testing Methodology • Altering Data to Simulate Recession Conditions • Adversarial Example Search • Residual Analysis for XGBoost • Analysis and Visualizations of Residuals • Segmented Error Analysis • Modeling Residuals • Remediating the Selected Model • Overemphasis of PAY_0 • Miscellaneous Bugs • Conclusion • Resources

دیباگ کردن یک طبقه‌بند تصویر PyTorch

Concept Refresher: Debugging Deep Learning • Debugging a PyTorch Image Classifier • Data Quality and Leaks • Software Testing for Deep Learning • Sensitivity Analysis for Deep Learning • Remediation • Sensitivity Fixes • Conclusion • Resources

تست و اصلاح سوگیری با XGBoost

Concept Refresher: Managing ML Bias • Model Training • Evaluating Models for Bias • Testing Approaches for Groups • Individual Fairness • Proxy Bias • Remediating Bias • Preprocessing • In-processing • Postprocessing • Model Selection • Conclusion • Resources

تیم قرمز برای XGBoost

Concept Refresher • CIA Triad • Attacks • Countermeasures • Model Training • Attacks for Red-Teaming • Model Extraction Attacks • Adversarial Example Attacks • Membership Attacks • Data Poisoning • Backdoors • Conclusion • Resources

چگونه در یادگیری ماشین پرخطر موفق شویم

Who Is in the Room? • Science Versus Engineering • The Data-Scientific Method • The Scientific Method • Evaluation of Published Results and Claims • Apply External Standards • Commonsense Risk Mitigation • Conclusion • Resources