Hall P. Machine Learning for High-Risk Applications

New York: O’Reilly Media, Inc., 2021. — 112 p.Who Should Read This BookWhat Readers Will LearnPreliminary Book Outline.
Bringing it All Together.
Conventions Used in This Book.
Using Code Examples.
O’Reilly Online Learning.
How to Contact Us.Contemporary Model Governance.
Basic Legal Obligations.
AI Incidents.
Organizational and Cultural Competencies for Responsible AI.
Accountability.
Drinking Your Champagne.
Diverse and Experienced Teams.
“Going Fast and Breaking Things”.
Organizational Processes for Responsible AI.
Forecasting Failure Modes.
Model Risk Management.
Beyond Model Risk Management.
Case Study: Death by Autonomous Vehicle.
Fallout.
An Unprepared Legal System.
Lessons Learned.
How to Red-Team AI Systems.
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 AI Security Concerns.
Counter-measures.
Model Debugging for Security.
Model Monitoring For Security.
Privacy-enhancing Technologies.
Robust Machine Learning.
General Countermeasures.
Case Study: Real-world Evasion Attacks.
Lessons Learned.
Resources.
Debugging AI Systems for Safety and Performance.
Training.
Reproducibility.
Data Quality and Feature Engineering.
Model Specification.
Model Debugging.
Software Testing.
Traditional Model Assessment.
Residual Analysis for Machine Learning.
Sensitivity Analysis.
Benchmark Models.
Machine Learning Bugs.
Remediation: Fixing Bugs.
Deployment.
Domain Safety.
Model Monitoring.
Case Study: Remediating the Strawman.
Resources.