Fall 2024 AI Topic Course: Trustworthy AI Foundations

Lectures: Wednesday 12:10-1:30 and Friday 2-3:20pm in Richard Weeks Hall on Busch campus room 208

Instructor: Ruixiang Tang

Office Hours: Friday 3-4:00pm, Hill Center room 416

Course Overview

This graduate topic course aims to give students a broader view of Trustworthy AI and focuses on understanding advanced techniques. The course covers key topics such as adversarial attacks and defenses, bias detection and mitigation, AI privacy, uncertainty estimation, and interpretable AI. Students will engage with the latest research, participate in discussions, and develop skills in critically analyzing and presenting complex material.

Prerequisites: This course will assume fundamental knowledge in AI and machine learning (e.g., 01:198:440 - Introduction to Artificial Intelligence, 16:198:536 - Machine Learning, or equivalent) and mathematical maturity (comfortable with linear algebra, probability, or equivalent). Students are expected to read and discuss research papers. Please contact the instructor if you have questions regarding whether your background is suitable for the course.

Grading


We will have 5 short quizzes and one final project. The final project can be done individually or in groups of no more than 3. Presentations will take place during the last three weeks of the semester, followed by a Q&A session. 


For the final project, students are required to choose a research paper on trustworthy AI, independently replicate its results, and critically analyze it to identify any limitations or potential areas for improvement. Students will then propose and develop a solution to address the identified issue or introduce a novel idea to enhance the research.

Course Schedule (tentative)

Week#

Topic

Notes

Recommended Papers for Further Reading

Week 1

Introduction to Trustworthy AI

Overview of course objectives

Importance of Trustworthy AI

Key concepts and definitions

Week 2

Foundational Concepts in Deep Learning

Basic Knowledge of Deep Learning

Feedforward, Backpropagation

MLP, CNN, RNN,Transformer

Week 3

Interpretable AI - Part 1

Introduction to Interpretable AI

Challenges of interpretability

Techniques for XAI

Week 4

Interpretable AI - Part 2

Advanced methods for XAI

Case studies and applications

Evaluation of interpretability

Week 5

Bias Detection and Mitigation for AI Models- Part 1

Understanding bias in AI models

Sources of bias

Methods for detecting bias

Week 6

Bias Detection and Mitigation for AI Models - Part 2

Techniques for mitigating bias

Fairness in AI

Ethical considerations

Week 7

Adversarial Attacks and Defenses for AI Models - Part 1

Introduction to adversarial attacks 

Types of adversarial attacks 

Case studies and examples

Week 8

Adversarial Attacks and Defenses for AI Models - Part 2

Defense against adversarial attacks

Evaluation of defense strategies

Practical applications and challenges

Week 9

AI Privacy

Introduction to AI privacy

Privacy Attack

Privacy-preserving techniques

Week 10

Trustworthy LLM - Part 1

Safety Alignment

Jailbreaking Attack and Defense

Multimodal Attack and Defense

Week 11

Trustworthy LLM - Part 2

Hallucination Detection and Mitigation

Uncertainty Estimation

Week 12

Guest Lecture / Industry Speaker

Invited talk from a leading expert in Trustworthy AI 

Discussion and Q&A session

Week 13

Student Presentations - Part 1

Student presentations

Discussion and feedback

Week 14

Student Presentations - Part 2

Student presentations

Discussion and feedback

Week 15

Student Presentations - Part 3

Student presentations

Discussion and feedback