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AI

AI Security, Compliance, and Explainability Training

Group Training + View more dates & times

                 
Overview

This Artificial Intelligence (AI) course covers the essential principles of AI ethics, regulatory compliance, and the challenges of AI security.

In this AI course, attendees learn AI’s role in various sectors, best practices for system security, and the intricacies of AI design and deployment. Students also explore the AI auditing processes and understand the importance of making AI transparent through explainability techniques. This course is ideal for professionals seeking a straightforward understanding of responsible AI development.

Objectives

  • Understand the importance of machine learning interpretability
  • Explore different types of ML interpretability models
  • Analyze standard techniques and methods for explainability
  • Evaluate the effectiveness of interpretability methods
  • Apply XAI in various sectors

Duration

2 days

Who Should Take This Course

Audience

  • AI and Machine Learning Practitioners
  • IT Regulatory and Compliance Officers
  • Cybersecurity Professionals
  • Decision Makers and Executives

Prerequisites

Students should have:

  • Foundational Knowledge in AI and Machine Learning
  • Familiarity with Data Management
  • Basic Cybersecurity Concepts

Software Requirements

Students should have Zoom installed as the conference platform.

Schedule
Course Outline
  • 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
FAQs
Is there a discount available for current students?

UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Asynchronous courses are excluded from this offer.

What is the cancellation and refund policy?

Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.

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