Computational Ethics and Large Language Models

UCLA CS 260E, Fall 2026

M/W 12-1:50pm, Boelter Hall 2444

Instructor: Saadia Gabriel
Email: skgabrie@cs.ucla.edu
Office: Eng VI 295A
Office Hours: 11-11:50am on Mondays

Course Description: Large language models (LLMs) are becoming ubiquitous. They are used in many real-world applications, ranging from content moderation and online advertisement to health care. Given their increasing role in what people see, how they think, and what is publicly known about them, it is critical to consider the ethical ramifications of deploying LLM-based systems. This course offers a lens on historical and current computational ethics problems in natural language processing (NLP). Together we will explore how large-scale language modeling is impacting domains such as privacy, health care, and political science. Discussions are accompanied by guest lectures from domain experts.

Schedule:

Date Topic Description Assignment(s)
9/28 Intro   We will go over the syllabus, schedule, reading list and course expectations. There will be an overview of historical challenges. [Slides]
  • Reading assignment #1, due by 10/11 11:59pm PT.
  • Sign up for a presentation slot!
9/30 AI & the Internet Lecture and in-class demo [Slides]
  • Coding assignment #1, due by 10/11 11:59pm PT.
10/5-10/7 Group project brainstorming Free time to meet in-person and coordinate final project plans. Guidelines for the final project proposal are here.
  • Final project group assignment form, due by 10/18 11:59pm
10/12 AI & the Internet Student Presentations
  • Reading assignment #2, due by 10/18 11:59pm PT.
10/14 Guest Lecture Celeste Oon (USC)
  • Reading assignment #3, due by 10/20 11:59pm PT.
10/19 AI & the Internet Student Presentations
10/21 AI, Privacy & Security Student Presentations
  • Reading assignment #4, due by 11/3 11:59pm PT.
10/26-10/28 No class (EMNLP) Peer feedback on proposals and collaborative hybrid discussion
  • Mid-quarter progress report, due by 11/3 11:59pm PT.
11/2 Guest Lecture Jillian Fisher (UW/Stanford)
  • Reading assignment #5, due by 11/8 11:59pm PT.
11/4 Multi-Agent Safety AI Student presentations
  • Reading assignment #6, due by 11/17 11:59pm PT.
11/9 AI & Human Interaction Student presentations and in-class demo
  • Reading assignment #7, due by 11/22 11:59pm PT.
  • Coding assignment #2, due by 11/17 11:59pm PT
11/11 No class (holiday)
  • Final project slides due by 11/24 11:59pm PT.
11/16 Guest Lecture Emilio Ferrara (USC)
11/18 AI Auditing & Healthcare Student presentations
11/23 AI Auditing Student presentations & concluding remarks
11/25 Final Presentations Schedule TBD
11/30 Guest Lecture Chinasa Okolo (Technecultura)
12/2 Final Presentations Schedule TBD
  • Final project papers due by 12/11 11:59pm PT.

Resources:

We will be using Perusall for collaborative paper note-taking and course discussion.

Grading:

Detailed guidelines for assignments will be released later in the quarter.

  • Reading Assignments (48%)
    • Students will read the assigned papers and post an original comment or question for each paper on Perusall. (42%)
    • In groups of 3, students will sign up to present one of the assigned papers in class and summarize online discussion from Perusall. Each student will only present once. (4%)
    • Students will sign up to be an in-class discussion leader for one paper they are not presenting. (2%)
  • Coding Assignments (10%)
    • We will have two in-class demos that provide a technical walkthrough of challenges in responsible language modeling. Students will complete a portion of the assignment after class and submit their results on BruinLearn.
  • Final Project (42%)
    • Students will form groups of 4-5 and write a short (max 5 pg) paper on an AI policy framework for addressing concerns raised during one of the class discussions.
    • This will be graded based on submitting a form with their group members and a proposal abstract (2%), mid-quarter progress report (5%), final in-person presentations (10%) and a final write-up (20%).
    • Additionally, students will provide short, constructive feedback to their peers' paper drafts and final presentations that can aid in finalizing project write-ups. (5%)

Course Policies:

Late Policy. Out of courtesy to peers, it's expected that students complete reading assignments on time, but students may turn in 1 reading assignment up to a week late without penalty. Since the final project is a group assignment there are no late days, but extensions will be considered under extraordinary circumstances. Students are expected to communicate potential presentation conflicts (e.g. illness, conference travel) to the instructor in advance.

Academic Honesty. Reading assignments are expected to be completed individually outside of the paper presentation and the instructor will check for overlap between posted comments/questions. For all assignments, any collaborators or other sources of help should be explicitly acknowledged. Violations of academic integrity (please consult the student conduct code) will be handled based on UCLA guidelines.

Accommodations. Our goal is to have a fair and welcoming learning environment. Students should contact the instructor at the beginning of the quarter if they will need special accomodations or have any concerns.

Use of ChatGPT and Other LLM Tools. Students are expected to first draft writing without any LLMs and all ideas presented must be their own. Students may use LLMs for grammer correction and minimal editing if they add an acknowledgement of this use. Any work suspected to be entirely AI-generated will be given a grade of 0.

Acknowledgements: This course was very much inspired by 2 UW courses: Yulia Tsvetkov's Ethics in AI course and Amy X. Zhang's Social Computing course. It was also inspired by Marzyeh Ghassemi's Ethical ML in Human Deployments course at MIT.