CMPM 118 (AIEA) - Collaborative Research Experiences in the AI Explainability and Accountability (AIEA) Lab
The full syllabus for the course for Spring 2026 is on Google Drive
There are two tracks:
- Auditor track this is for new undergraduate students that are interested in joining the AIEA Lab. To be eligible to join the lab as an active member (see below) you must complete the 10 onboarding tasks within a quarter.
- Active track this is for ongoing undergraduate students that have (a) taken at least one quarter for CMPM 118 prior and (b) completed the 10 onboarding tasks for a project within a quarter.
Leilani H. Gilpin (she/her), Assistant Professor of CSE Email: [email protected] Office: E2 347B Office Hours: By appointment
I am excited about teaching intelligent systems to explain themselves. I joined the faculty of UCSC in 2021 as an Assistant Professor in Computer Science and Engineering. I previously developed a new, short course on AI and global risks at MIT, and was a teaching assistant for Large Scale Symbolic Systems. I typically teach AI (CSE 140, CSE 240) and responsible data science (CSE 246) and Machine Learning (CSE 142, CSE 242).
The course is structured around a set of deliverables, meetings, and status forms. Students will work either individually or in small teams of 2-4 persons to complete a set of 10 tasks over the 10-weeks quarter. The expected commitment is ~10 hours per week. Each task will require a set of deliverables to be submitted to Canvas (e.g., presentation, report, code, results).
The full grading guidelines are available here.
Students interested in joining the AIEA lab as an active member must complete all the tasks of their chosen project, regardless of the number of points earned, as these tasks are the basics of the research projects carried on by our lab. All the students who complete the 10 deliverables by the end of the quarter (the last day of instruction) will be invited to become an active lab member of the AIEA lab.
The available projects for Spring 2026 are:
- Robustifying Autonomous Vehicles (AVs)
- NeuroSymbolic AI (NeSy)
- Explanatory Educational Tools (XEdu)
- Self-Explaining Neural Networks (SENNs)