AAAI 2020 Tutorial on Recent Advances in Fair Resource Allocation

February 8, 2020, 8:30am - 12:30pm
At the AAAI Conference on Artificial Intelligence, New York City, New York.

Organizers: Rupert Freeman and Nisarg Shah.


Fairness in algorithmic decision making has received growing attention recently. However, fairness in the context of resource allocation has been formally studied for many decades in microeconomics, and for a few decades in computer science. This tutorial will present an overview of the literature on fair resource allocation. It will focus on the various definitions of fairness proposed in the literature and the algorithms that guarantee these fairness notions. The focus will be on recent advances, but no prior background will be required.


8:30am-10:30am: The first part of the tutorial will look at the classic setting of cake-cutting, which models allocation of a divisible resource. This part will cover classic fairness notions such as proportionality, envy-freeness, equitability, and Pareto optimality, and explore their interplay with game-theoretic notions such as strategyproofness. An overview of fair cake-cutting algorithms will also be presented. This part will end with a discussion on connections between fairness and market equilibria.

11:00am-12:30pm: The second part of the tutorial will focus on the allocation of indivisible items. This will cover relaxations of proportionality, envy-freeness, and equitability, as well as other fairness notions such as maximin share guarantee. This part will explore the generalization of individual-level fairness to group-level fairness, the use of different preference classes (e.g., additive preferences, combinatorial preferences, and ranked preferences), various real-world constraints, static versus dynamic allocations, and different types of items (e.g., public versus private resources and goods versus chores).