All contents in this document are tentative.
- Zoom Link
- Google Drive
- All the lecture materials and assignments will be shared in the google drive.
- [20240228] No offline class today; Instead, watch the recorded video.
- [20240304] No offline class today; Instead, watch the recorded video.
- [20240306] No Class ! Have a break :)
- [20240311] Submit Google Forms about team sign-up. (Group) by 23:59 Mar 11(Mon).
- [20240226] Submit Lecture 1 Survey
- [20240226] Join slack channel: Invitation Link
- Lecturer: Alice Oh
- TA: Juhyun Oh (411juhyun@kaist.ac.kr), Eunsu Kim (kes0317@kaist.ac.kr)
- Contact: Through slack "qa" channel
- Mon/Wed 14:30 PM - 16:00 PM
- Rm. 2445, E3-1 (Information Science and Electronics Bldg.)
- Knowledge of machine learning and deep learning (CS376, CS470, or CS570)
| # | Date | Topic | Reading | Presenter | Notes |
|---|---|---|---|---|---|
| 1 | 2/26 | Introduction to AI Ethics | Lecturer | ||
| 2 | 2/28 | Overview | Lecturer | Recorded Video | |
| 3 | 3/4 | Generative AI | Lecturer | Recorded Video | |
| 3/6 | NO CLASS | ||||
| 4 | 3/11 | Overview | Lecturer | Form teams & sign-up | |
| 5 | 3/13 | Generative AI | Lecturer | ||
| 6 | 3/18 | Bias & Fairness | Lecturer | ||
| 7 | 3/20 | Mitigating Social Harms in LLMs | Invited Lecturer (Sachin Kumar) | ||
| 8 | 3/25 | Safety (toxicity, jailbreak) | Students | ||
| 9 | 3/27 | Safety (toxicity, jailbreak) | Students | ||
| 10 | 4/1 | Truthfulness (misinformation, hallucination, sycophancy) | Students | ||
| 11 | 4/3 | Truthfulness (misinformation, hallucination, sycophancy) | Students | ||
| 4/8 | Project Proposal | All students | Recorded Video | ||
| 4/10 | NO CLASS | General Election (국회의원 선거) | |||
| 4/15 | NO CLASS | Midterm Exam Period | |||
| 4/17 | NO CLASS | Midterm Exam Period | |||
| 12 | 4/22 | Privacy Issues in Data & Models | Students | ||
| 13 | 4/24 | Privacy Issues in Data & Models | Students | ||
| 14 | 4/29 | Transperacy & Limitation of Current Gen AI | Students | ||
| 15 | 5/1 | Explainable AI | Students | ||
| 5/6 | NO CLASS | Substitute Holiday (어린이날) | |||
| 5/8 | Project Progress | Students | |||
| 16 | 5/13 | Societal Impact & AI Divide | Students | ||
| 17 | 5/20 | AI for Social Good | Students | ||
| 5/22 | NO CLASS | Substitute Holidays (부처님오신날) | |||
| 18 | 5/27 | Societal Impact & Environment | Students | ||
| 19 | 5/29 | Wrap-up | Lecturer | ||
| 6/3 | Final Presentations | All students | In class | ||
| 6/5 | Final Presentations | All students | In class | ||
| 6/10 | NO CLASS | Final Exam Period | |||
| 6/12 | NO CLASS | Final Exam Period |
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 Prompt | Write 3 discussion prompts about a paper | 2x | Individual |
| Discussion Presentation | Present the discussion of the paper based on their report | 1x | Team |
| Paper Reading Reflections | Write reflections of the paper | 2x | Individual |
Lecturer or student groups will give a lecture on each topic by each day.
Students will read, present, and think about the latest research from the reading list published in AI and ML conferences (e.g., NeurIPS, ICLR, ACL, CVPR, FAccT) related to ethical considerations. Readings may also include blog posts, articles in the media, online forum discussions, and publications from global governing bodies.
- Choose a paper related to the lecture topic from the reading list.
- Read the paper before the discussion and write a 1-page reflection on the paper, including a summary, strengths, limitations, and suggestions.
Students will lead peers to discuss the readings with thought-provoking questions. You will challenge the findings in the articles as to their accurate reporting and interpretation; you will discuss relevance to the current time and various locales with different cultural backgrounds. You will present and discuss ideas for future research directions in AI and ethics.
- 20 in-class discussions (see schedule).
- Organize a group of 3-4 people, and have time to present what you read and discuss
- All groups should submit their result at the end of class.
- See the details on this page.
Team project will be a major part of the class, especially during the second half. Projects will be basically replications or modifications of recent research in AI Ethics. See the details on this page.
- If you miss up to 2 classes, there will be no penalty. After 2, points will be taken off. Because you can miss up to 2 for free, we will not take any excuses for missing the class (unless you have a special case, such as prolonged sickness, in which case you should 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.
- Participation and Attendance: 20%
- In-class Discussion / Reading Reflections: 30%
- Project: 50%