AI Security, Compliance, and Explainability Training

• Chapter 1 – Ethics and Regulation
  • What is an AI System?
  • View of AI System
  • AI System Classifications
  • Branches of AI Today
  • AI by the numbers
  • AI – the Good
  • AI – the Bad
  • Principles of AI Ethics
  • Principles of AI Ethics
  • Fairness
  • Accountability
  • Transparency
  • Explainability
  • Privacy and autonomy
  • Reliable
  • Ask ChatGPT 3.5
  • AI Ethics in Practice
  • Regulatory Compliance in AI Systems
  • What are the benefits of AI regulation?
  • What are the disadvantages of regulating AI
  • Regulations and standards in AI
  • GDPR and data protection
  • AI in healthcare (HIPAA and other relevant laws)
  • AI in healthcare examples
  • AI in finance and regulatory compliance
  • US FINRA AI Deployment
  • AI in US finance examples
  • AI in the global finance examples
  • Case studies of AI non-compliance
  • Lab
  • Addressing Regulatory and Compliance
  • Dangers of Discrimination and Bias
  • Data Security and Data Privacy
  • Control and Security Concerns of AI
  • Cooperative Corporate Compliance
  • Summary
• Chapter 2 – Security and Privacy
  • What is AI Cybersecurity?
  • Threats and challenges in AI security
  • Implementing AI in cybersecurity
  • Adversarial attacks
  • Model inversion and extraction
  • Data poisoning
  • Best practices for securing AI systems
  • Robustness techniques
  • Differential privacy
  • Federated learning
  • Homomorphic encryption
  • Summary
• Chapter 3 – Secure AI Design and Deployment
  • Secure Software Development
  • Connectivity
  • Exploitation of AI Systems (Jailbreaks)
  • Infrastructure Concerns
  • System Vulnerabilities
  • Data Privacy
  • Data Leaks via Generating Text
  • OpenAI GPT-3/4 Data Location and Storage
  • Azure OpenAI
  • Adversarial Attacks
  • Malicious Use of AI
  • Bias and Discrimination
  • Regulatory and Ethical Considerations
  • Security and Privacy in Chatbots
  • Ensuring Security and Privacy
  • Data Protection
  • Enforcing Data Protection
  • Anonymization Techniques
  • Best Practices for Security with Generative AI
  • Sources of Bias in AI
  • Tackling AI Bias
  • Real-world Case Studies
  • Autonomous Vehicles and the Trolley Problem
  • AI in Warfare and Weaponization
  • AI in Criminal Justice
  • Summary
• Chapter 4 – AI Auditing and Certification
  • Introduction
  • Organizational Roles in AI Ethics and Compliance
  • Implementing AI Ethics Guidelines and Checklists
  • Key Components of an AI Audit
  • Steps in the AI Auditing Process
  • Post-Deployment Monitoring and Feedback Loops
  • Reporting and Recommendations
  • AI Certification Process
  • Summary
• Chapter 5 – Explainable AI (XAI)
  • Introduction to Machine Learning Interpretability
  • Importance of ML interpretability
  • Different types of ML interpretability models
  • Model-agnostic interpretability methods
  • Model-specific interpretability methods
  • Limitations of model-specific interpretability
  • Limitations of Model-agnostic interpretability
  • Global vs. Local interpretability
  • Interpretability in Deep Learning
  • Techniques and Methods for Explainability
  • Layer-wise relevance propagation (LRP)
  • Sensitivity analysis
  • Gradient-weighted class activation mapping (Grad-CAM)
  • Evaluating Interpretability
  • Techniques for evaluating interpretability
  • Overview of existing evaluation frameworks
  • Model-Agnostic Visual Analytics (MAVA)
  • Human-AI Collaborated Evaluation (HACE)
  • Interpretability in Large Language Models
  • Interpretability in Generative LLM’s
  • Common evaluation metrics for generative AI models
  • Common evaluation metrics – Diversity metrics
  • Common evaluation metrics – Likelihood
  • Common evaluation metrics – Perplexity
  • Common evaluation metrics – Inception Score
  • Common evaluation metrics – FID
  • Common evaluation metrics – BLEU
  • Common evaluation metrics – ROUGE
  • Common evaluation metrics – Human evaluation
  • Techniques for Interpreting Large Language Models
  • Importance of XAI in various sectors
  • XAI in Healthcare: Enhancing Care and Transparency
  • XAI in Finance: Driving Decisions and Building Trust
  • XAI in Legal Systems: Fairness and Accountability
  • Summary

Lab Exercises

• Lab 1. AI Ethics and Regulation
• Lab 2. Understanding security and privacy
• Lab 3. Learning the CoLab Jupyter Notebook Environment
• Lab 4. Guardrails with template manual
• Lab 5. Guardrails with system prompt
• Lab 6. Optional – Implementing Nemo Guardrails for LLM Response Restriction Overview
• Lab 7. Designing an Audit Process for OpenAI’s ChatGPT
• Lab 8. AstroZeneca Ethics-Based AI Audit Framework Design
• Lab 9. Lab 1 – Designing a Gender Bias Test for a Large Language Model (LLM)
• Lab 10. Exploring Machine Learning Interpretability (MLI) with H2O’s Driverless AI Overview