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:

1. Fairness in game theory;
2. Fair division algorithms; and
3. Algorithmic fairness.

This course aims to equip students with a nuanced understanding of fairness across various domains, blending theoretical insights with practical applications.