Algorithmic Fairness in Multiagent Systems

Penn State | IST 597: Special Topics on AI/DS | Fall 2026

Time and location: Wednesdays 2:30 - 5:30 pm
Instructor: Hadi Hosseini
Office hours: See Canvas (or on Zoom upon request)

Algorithmic Fairness in Multiagent Systems is a graduate course that xplores the theory and algorithms behind fair and strategic decision-making in multiagent systems, drawing on AI, economics, and computation. Students study how agents—humans, institutions, or AI-powered systems—with diverse preferences and values interact to produce collective outcomes, with a focus on fairness, efficiency, and incentive-compatible mechanisms. Core topics include game theory, mechanism design, social choice, preference aggregation, fair division, and matching theory, with applications ranging from crowdsourcing and healthcare resource allocation to federated AI systems.

Evaluation: course evaluation is based on homework assignments (2 x 10%), paper critiques (20%), participation (15%), and a course project (45%). The projects should include solid and non-trivial implementations and/or novel research questions.

Course Schedule

Projects & Homework

Textbooks

The following books are not required but highly recommended. These books are generally available online for free.

Handbook of Computational Social Choice
Felix Brandt, Vincent Conitzer, Ulle Endriss, Jérôme Lang, and Ariel D. Procaccia, Cambridge University Press, 2016.
Economics and Computation: An Introduction to Algorithmic Game Theory, Computational Social Choice, and Fair Division
Editors Jörg Rothe, Springer-Verlag Berlin Heidelberg 2016.
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Kevin Leyton-Brown and Yoav Shoham, Cambridge University Press.

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