Patrick Hall,
James Curtis,
Parul Pandey
Machine Learning for High–Risk Applications
Approaches to Responsible AI
Paperback Engels 2023 1e druk 9781098102432Levertijd ± 16 werkdagen
Gratis verzonden
Samenvatting
The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks.
This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.
- Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security
- Learn how to create a successful and impactful AI risk management practice
- Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework
- Engage with interactive resources on GitHub and Colab
Specificaties
ISBN13:9781098102432
Taal:Engels
Bindwijze:paperback
Aantal pagina's:350
Uitgever:O'Reilly
Druk:1
Verschijningsdatum:4-5-2023
Hoofdrubriek:IT-management / ICT
Lezersrecensies
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Inhoudsopgave
Foreword
Preface
Who Should Read This Book
What Readers Will Learn
Alignment with the NIST AI Risk Management Framework
Book Outline
Part I
Part II
Part III
Example Datasets
Taiwan Credit Data
Kaggle Chest X-Ray Data
Conventions Used in This Book
Online Figures
Using Code Examples
O’Reilly Online Learning
How to Contact Us
Acknowledgments
Patrick Hall
James Curtis
Parul Pandey
I. Theories and Practical Applications of AI Risk Management
1. Contemporary Machine Learning Risk Management
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
2. Interpretable and Explainable Machine Learning
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
Preface
Who Should Read This Book
What Readers Will Learn
Alignment with the NIST AI Risk Management Framework
Book Outline
Part I
Part II
Part III
Example Datasets
Taiwan Credit Data
Kaggle Chest X-Ray Data
Conventions Used in This Book
Online Figures
Using Code Examples
O’Reilly Online Learning
How to Contact Us
Acknowledgments
Patrick Hall
James Curtis
Parul Pandey
I. Theories and Practical Applications of AI Risk Management
1. Contemporary Machine Learning Risk Management
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
2. Interpretable and Explainable Machine Learning
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
3. Debugging Machine Learning Systems for Safety and Performance
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
4. Managing Bias in Machine Learning
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
5. Security for Machine Learning
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
II. Putting AI Risk Management into Action
6. Explainable Boosting Machines and Explaining 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
7. Explaining a PyTorch Image Classifier
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
8. Selecting and Debugging XGBoost Models
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
9. Debugging a PyTorch Image Classifier
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
10. Testing and Remediating Bias with 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
11. Red-Teaming 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
III. Conclusion
12. How to Succeed in High-Risk Machine Learning
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
Index
About the Authors
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
4. Managing Bias in Machine Learning
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
5. Security for Machine Learning
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
II. Putting AI Risk Management into Action
6. Explainable Boosting Machines and Explaining 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
7. Explaining a PyTorch Image Classifier
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
8. Selecting and Debugging XGBoost Models
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
9. Debugging a PyTorch Image Classifier
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
10. Testing and Remediating Bias with 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
11. Red-Teaming 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
III. Conclusion
12. How to Succeed in High-Risk Machine Learning
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
Index
About the Authors
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