See LionPATH to schedule.
Course Topic: Algorithmic Fairness and Mechanism Design
This graduate-level course explores 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.
Graduate students (Master’s or PhD) from any program are welcome, along with undergraduate students in their third year or higher.
