Machine Learning for High-Risk Applications 1st Edition by Patrick Hall, James Curtis, Parul Pandey ISBN 9781098102395 1098102398

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Product details:

ISBN 10: 1098102398
ISBN 13: 9781098102395
Author:  Patrick Hall, James Curtis, Parul Pandey

Table of contents:

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

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