cs50705-ethics-spring-2026

[CS.50705] AI Ethics / Spring 2026

All contents in this document are tentative.

Zoom Link
- This link should be used for all online classes.
- For classes not specified as online, please attend in person at Room 210, E3-5 (KRAFTON Building).
Google Drive
- All the lecture materials and assignments will be shared in the google drive.
Please join the course slack channel for announcements and Q&A.

Knowledge of machine learning and deep learning (CS.30706, CS.40700, or CS.50700)

#	Date	Topic	Presenter	Notes
1	3/2	NO CLASS		March 1st Independence Movement Day (Substitute Holiday)
2	3/4	Introduction to AI Ethics	Lecturer	Online
3	3/9	LLM Overview	Lecturer
4	3/11	LLM Overview	Lecturer	Team Signup Due
5	3/16	TBD	Lecturer
6	3/18	Bias & Fairness	Students	Online
7	3/23	Bias & Fairness	Students
8	3/25	Safety (toxicity, jailbreak)	Students
9	3/30	Safety (toxicity, jailbreak)	Students
10	4/1	Safety (multimodal, deepfake)	Students	Online / Project Proposal Presentation Due
11	4/6	Truthfulness (misinformation, hallucination, sycophancy)	Students	Online
12	4/8	Truthfulness (misinformation, hallucination, sycophancy)	Students
13	4/13	Privacy Issues in Data & Models	Students
14	4/15	Model/Data Transparency	Students
15	4/20	Project Progress Presentation (Online)
16	4/22	Project Progress Presentation (Online)
17	4/27	Explainable AI	Students
18	4/29	Multilingual & Multicultural AI	Students
19	5/4	Multilingual & Multicultural AI	Students
20	5/6	Societal Impact & AI Divide (global adoption, AI literacy)	Students
21	5/11	Human Intelligence Vs. Artificial Intelligence	Students
22	5/13	Human Intelligence Vs. Artificial Intelligence	Students
23	5/18	AI Agents	Students
24	5/20	AI Dependency (mental health, education)	Students
25	5/25	NO CLASS		Buddha’s Birthday (Substitute Holiday)
26	5/27	Societal Impact & Environment	Students
27	6/1	AI for Social Good	Students
28	6/3	NO CLASS		Local Election (Holiday)
29	6/8	AI for Social Good	Students
30	6/10	Wrap Up	Lecturer
31	6/15	Project Final Presentations	All Students
32	6/17	Project Final Presentations	All Students	Final Report, Teamwork Report Due

This course includes lectures, readings, discussions, quizzes, and team projects. Students will be asked to do the following things.

Tasks	Descriptions
Project	Proposal, progress update, final presentation / Final report / Peer review / Teamwork report	1x	Team
Paper Presentation	30-minute presentation with 1 or 2 papers on a topic according to the schedule (will depend on the amount of content in the papers)	1x	Team
Discussion Presentation	Present the discussion of the paper based on their report	1x	Team
Paper Reading Reflection	Write reflections of the paper	4x	Individual
Paper Reading Quiz	Short in-class quiz on weekly paper	Weekly (Random)	Individual

Lectures will be delivered either by the instructor or by assigned student teams, depending on the topic and schedule.

The team project is a major part of this course, particularly during the second half of the semester.

Projects will include replications or modifications of recent research in AI Ethics.
Please find the details in the team project page.

Each team will read, analyze, and present recent research related to ethical issues in AI and machine learning.

Each team will select 1–2 papers related to the assigned lecture topic, following the course schedule and reading list.
Papers may come from major conferences (e.g., NeurIPS, ICLR, ACL, CVPR, FAccT) or other relevant sources, including blog posts, media articles, online discussions, or publications from global governing bodies.
Each team will prepare a 30-minute presentation that includes (but not limited to): a summary of the paper(s), key contributions and strengths, limitations or weaknesses, suggestions for future work.

Each class will include an in-class discussion based on the assigned readings.

The instructor will provide discussion questions for each class.
Each team will participate in the discussion and submit a written or transcribed record of their discussion by the end of class (AI-based transcription tools such as ClovaNote may be used).
For each class, one team will be assigned to present their discussion points and ideas at the end of the discussion session.
Please find the details in the discussion page.

All students are expected to read the paper(s) selected by the presentation team before each class.

Write a 1-page reflection on the paper, including a summary, strengths, limitations, and suggestions.
Students need to submit total four reflections until the end of the semester.

All students are expected to read the paper(s) selected by the presentation team before each class.

A short in-class reading quiz will be given on random days, consisting of 1–2 open-ended, thought-provoking questions.
The quiz serves both to encourage pre-class reading of the paper(s) to be presented and discussed, and as the attendance record for the class.
Lowest two quiz grades will be dropped.

Attendance is recorded through the paper reading quizzes.
We will not take excuses for absence (including conference travel, illness) since we drop lowest two quiz grades.
If you have a special case, such as prolonged sickness, email the teaching staff.
Unless otherwise specified, we will not accept late homework assignments, quizzes, peer evaluations, or project submissions. For exceptional individual circumstances, please contact the teaching staff.

Recent progress in large-scale language models (LLM), such as ChatGPT, motivates explicit policies.

The entire course policy is LLM-agnostic: no grader will ever evaluate your submission differently because they suspect it was generated by an LLM.
You are free to use an LLM as long as you acknowledge it.
Like any other online tool, you are ultimately responsible for whatever you submit.
You will be asked to state how you are assisted by LLM at the end of the semester to evolve in future courses.