CSEP 590B Explainable AI

General Information

Lecture time: Tuesdays, 6:30-9:20 pm

Location: Bill & Melinda Gates Center (CSE2) G10

Instructors: Su-In Lee and Ian Covert

Teaching assistants: Hugh Chen and Chris Lin

Office hours

  • Su-In Lee: Thursdays 5:00-6:00pm @ Zoom

  • Ian Covert: Sundays 8:00-9:00pm @ Zoom

  • Hugh Chen: Tuesdays 5:30-6:30pm @ Gates 131

  • Chris Lin: Fridays 4:30-5:30pm @ Zoom

*Note about the course materials

If you are teaching an XAI course and want to use any of our course materials, please feel free to reach out to us. We designed our slides and homeworks from scratch, and we hope they can be useful to many students and researchers working in this area.


This course is about explainable artificial intelligence (XAI), a subfield of machine learning that provides transparency for complex models. Modern machine learning relies heavily on black-box models like tree ensembles and deep neural networks; these models provide state-of-the-art accuracy, but they make it difficult to understand the features, concepts, and data examples that drive their predictions. As a consequence, it's difficult for users, experts, and organizations to trust such models, and it's challenging to learn about the underlying processes we're modeling.

In response, some argue that we should rely on inherently interpretable models in high-stakes applications, such as medicine and consumer finance. Others advocate for post-hoc explanation tools that provide a degree of transparency even for complex models. This course explores both perspectives, and we'll discuss a wide range of tools that address different questions about how models makes predictions. We'll cover many active research areas in the field, including feature attribution, counterfactual explanations, instance explanations and human-AI collaboration.

The course consists of 10 lectures (each session is 3 hours long) and is structured as follows:

  • Introduction and motivation (1 lecture)

  • Feature importance: removal-based explanations, propagation-based explanations, evaluation metrics (4 lectures)

  • Other explanation paradigms: inherently interpretable models, concept explanations, counterfactual explanations, instance explanations, neuron interpretation (3 lectures)

  • Human-AI collaboration (1 lecture)

  • Applications in industry (1 lecture)


Ed discussion board: https://edstem.org/us/courses/21664/discussion

Canvas: https://canvas.uw.edu/courses/1545385

Course email list: csep590b_sp22@cs.washington.edu


We won't use a textbook because there isn't one that covers enough content (although Christoph Molnar's online book is quite good). Instead, we'll directly reference recent research papers.

Machine learning resources

Andrew Ng’s lecture notes from Stanford CS 229

Kevin Jamieson’s course website for UW CSE 546

The Elements of Statistical Learning

Computer Age Statistical Inference


  • 50% Homework

  • 40% Paper discussion posts

    • Bonus: Leading discussion

  • 10% In-class participation


There are three homework assignments, plus one review assignment:

  • HW0: Warm-up (30 points) PDF, Latex source

    • Due: Week 2, 11:59pm Monday April 4

  • HW1: Feature importance (100 points) PDF, Latex source

    • Due: Week 5, 11:59pm Monday April 25

  • HW2: Gradient-based explanations and metrics (100 points) PDF, Latex source

    • Due: Week 8, 11:59pm Monday May 16

  • HW3: Inherently interpretable models and instance explanations (100 points) PDF, Latex source

    • Due: Week 10, 11:59pm Wednesday June 1

Collaboration policy: Students must submit their own answers and their own code for programming problems. Limited collaboration is allowed, but you must indicate on the homework with whom you collaborated.

Late policy: Homeworks must be submitted online on Canvas by the posted due date. The penalty for late work is 20 points per day, and each student gets 3 free late days for the quarter.

Lecture schedule & reading