CAREER: Robust Fairness in Matching Markets

Resource allocation presents significant decision-making challenges across multiple critical applications in society. Matching markets establish a principled framework for distributing goods, services, and information among interested parties---humans, institutions, nations, or autonomous agents---in a robust and efficient manner. In many matching and resource allocation settings such as school choice, refugee placement, food donation, and ridesharing the use of monetary transfers is prohibited, or simply infeasible, due to ethical, societal, or legal concerns. The broad objective of this project is developing a theoretically grounded approach for robust fairness in practical and large-scale allocation markets through the integration of Artificial Intelligence (AI), economics, and computation. In particular, this research addresses fairness issues that arise due to uncertain or incomplete information, dynamic nature of markets, complex constraints, and strategic behavior of participating entities that influence the outcome. This project will lead to the creation of novel fair and efficient AI systems across practical domains such as federated healthcare and gig economies. This project will also lay the foundation for implementing fair systems for matching students to advisors, assigning mentors to mentees, and conference scheduling in a principled and robust manner. It will integrate allocation solutions through a publicly accessible software platform to foster the adoption of sound fair solutions in everyday decision-making, and will curate a valuable learning resource to reach the public and practitioners beyond academia and educators.

This project makes a systematic effort in designing novel fair AI systems by bridging the gap between the fields of matching theory and fair division and enabling the creation of new approximate algorithms. It aims at making advances in the following interconnected directions: (i) Matching and allocation under uncertain/noisy preferences, that aims at devising fair solutions that are robust to uncertainty or noise in preference information, (ii) Fairness and diversity in dynamic matching, that combines techniques from online matching and fair division to develop solutions that provide robust fairness and diversity guarantees in dynamic markets, and (iii) Incentives and fairness in two-sided markets, that provides a systematic approach to blend techniques from stable matching markets with recent advances in fair division to design robust solutions in the presence of strategic behavior.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.