Course Policies
Table of contents
About
Discussions about fairness have long been prevalent in fields such as game theory, social choice theory, political philosophy, and ethics. More recently, there is a growing focus in AI (especially machine learning) on creating algorithms that are fair. This course is divided into three parts, each focused on different ways that fairness considerations influence individual and group decision-making:
- Fairness in game theory;
- Fair division algorithms; and
- Algorithmic fairness.
This course aims to equip students with a deep and nuanced understanding of fairness across various domains, blending theoretical insights with practical applications.
Resources
Readings: The readings for the course will be made available on the course website.
Discussion: You must submit weekly discussion posts on ELMS.
PollEverywhere: We will use PollEverywhere for in-class quizzes and surveys. You can sign-up (for free) using the following link.
Gradescope: We will use Gradescope for your problem sets. You should access gradescope through the ELMS website. You already are added to the Gradescope gradebook.
Course Requirements
The course requirements are:
Participation: There will be a number of short quizzes given during each lecture. Typically, these short quizzes will consist of 1 question that is given during class using PollEverywhere, but some lectures may have more than 1 question. Make-up quizzes will not be offered. I will drop the lowest 5-10\% of the quizzes (so you can miss some of the questions without losing any points).
*Discussion: Students must submit a weekly discussion post and a response on ELMS. Each week, you will be asked to provide a question or reaction to some aspect of the reading for this week. If you have a question, then you should give some motivation from the reading that prompted the question. Your question or reaction should be approximately 200 words. After submitting your question or reaction, you should make at least 1 comment on another student’s question or reaction. Each discussion post is due Wednesdays at 11:59pm. You will. have until the following Monday at 11:59pm to respond to another post. I will drop the two lowest scores, so you can skip posting at most twice.
Problem Sets: There will be 3 problem sets (after each part). Problem sets will be submitted through Gradescope (accessible through the course website). You can use your notes, the readings, and the online textbook, but you should not discuss your answers with your classmates or use any AI tools, such as ChatGPT, to answer these questions.
Final exam: The final exam will be an in-class exam given during exam week. A review sheet containing the material that will be covered on the exam will be provided a week or two in advance of the exam.
Grades
Grades will be assigned according to the following weights:
| Activity | Percent |
|---|---|
| Participation | 20% |
| Discussion | 35% |
| Problem Sets | 35% |
| Final Exam | 10% |
See undergraduate catalogue for description of grades, e.g., A+, A, A-, etc.
Topics
Below is a list of topics that we will discuss during the semester.
- Ultimatum game and social preferences
- Nash bargaining game
- Evolutionary game theoretic models of fairness
- Criticisms of evolutionary game-theoretic models of fairness
- Fair division of indivisible goods
- Pardoxes of fair division
- Fair division of divisible goods
- Using lotteries to ensure fairness
- Fairness in group decision-making
- Fairness in machine learning
- Algorithmic fairness and statistical discrimination
Support
UMD has many resources available to help students. Below are links to some resources that you might find helpful.