COMPSCI 690F Responsible Artificial Intelligence

The real-world deployment of machine learning models faces a series of lateral challenges affecting model trustworthiness, such as domain generalization, dataset shifts, causal validity, explainability, fairness, representativeness, and transparency. These challenges become increasingly important in techno-social systems affecting human high-stake decision making, which is often regulated by law. In this course, students will learn techniques for robust model evaluation, model selection, causal discovery, explainable and fair artificial intelligence, and interpretable models. In addition, students will reason about representativeness, transparency, and legal aspects of techno-social systems. The course will review both cutting-edge research and relevant portions of recent open-access textbooks. Coursework includes reading recent research papers, programming assignments, and a final group project. After completing the course, students should be able to develop, investigate, evaluate, and deploy artificial intelligence systems more responsibly.

 

Credits: 
3
Date: 
Tuesday, September 6, 2022 to Monday, December 12, 2022
Tuesday, September 5, 2023 to Friday, December 8, 2023
Tuesday, September 2, 2025 to Tuesday, December 9, 2025
Class meets on: 
Monday
Wednesday
Time: 
2:30 PM - 3:45 PM
Instructor: 
Eugene Bagdasarian
CompSci
Graduate
September, 2025