[CS575] AI Ethics / Spring 2023

Please send email to [email protected] regarding any class-related issues with "[CS575]" to the title.

All contents in this document are tentative.

Announcement

• [20230314] No offline class on Mar 22 (Wed); Instead, watch the recorded video and submit your discussion report on Google Drive by the end of the day (23:59, Mar 22)
• [20230314] Check out the schedule for paper presentation, discussion presentation, discussion questions, reading reflections
• [20230308] Submit two Google Forms (Group, Individual) by 23:59 today
• [20230306] No offline class today; Instead, watch the recorded video and submit your discussion report on Google Drive
• [20230227] Submit Lecture 1 Survey
• [20230227] Join slack channel: Invitation Link

COVID-19

Most classes will be in-person but will be recorded for those who are not able to attend. Some classes may be online. This course follows regulations regarding KAIST and the South Korean government.

Teaching Staff

• Lecturer: Alice Oh
• TA: Haneul Yoo
• Contact: [email protected]

Please send emails to [email protected]. We will not consider any class-related email arriving in our personal accounts. Please put "[CS575] to the title when you email. (e.g., [CS575] De we have a class on MM/DD?)

Time & Location

• Mon/Wed 10:30 AM - 12:00 PM
• Rm. 2443, E3-1 (Information Science and Electronics Bldg.)

Prerequisites

• Knowledge of machine learning and deep learning (CS376, CS470, or CS570)

Schedule (Subject to Change)

week	Day	Type	Topic	notes	Project
1	02/27	Introduction	Introduction	03/01 Holiday
2	03/06, 03/08	Discussion Discussion	Bias in AI/ML Systems		Choose Teams
3	03/13, 03/15	Discussion Discussion	Generative AI Tocixity and Misinformation in Generative Models
4	03/20, 03/22	Discussion Discussion	Privacy Issues in Data & Models
5	03/27, 03/29	Discussion Discussion	Explainable AI
6	04/03, 04/05	Discussion Discussion	Trustworthy AI
7	04/10, 04/12	Project Presentations Project Presentations			Proposal, Peer-review
8	04/17, 04/19	No Class (Midterm Exam)
9	04/24, 04/26	Discussion Discussion	Societal Impact & AI Divide
10	05/01, 05/03	Discussion Discussion	Societal Impact & Environment
11	05/08, 05/10	Project Presentations Project Presentations			Project Progress, Peer-review
12	05/15, 05/17	Discussion Discussion	AI for Social Good (Healthcare, Drug Discovery, and Food Production)
13	05/22, 05/24	Discussion Discussion	AI for Social Good (Environment, Education, and Democracy)
14	05/29, 05/31	Wrap-up Wrap-up
15	06/05, 06/07	Project Presentations Project Presentations			Final Presentation, Peer-review
16	06/12, 06/14	No Class (Final Exam)			Final Report

Course

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 a reflections of the paper	2x	Individual

Lecture

Lecturer or student groups will give a lecture on each topic by each day.

Reading Reflections

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.

Discussion

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 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

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.

Policy on Large Language Models

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.

Evaluation (Subject to Change)

• Participation and Attendance: 20%
• In-class Discussion / Reading Reflections: 30%
• Project: 50%