Course Supervisor: Professor Przemyslaw Grabowicz.

Overview

In a brand new graduate-level course, I crafted assignments, tutored students, and superived student projects centering research questions concerning interpretability, open-world learning, dataset sift, AI fairness, and causal discovery.

Course Description

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.

Class Page: see the up-to-date class page for COMPSCI 690F, here.