Before revision: Matching algorithms have been classically studied with the goal of finding stable solutions. However, in many important societal problems, the degree of fairness in the matching assumes crucial importance, for instance when we have to match COVID-19 patients to care units. We study the problem of finding a stable many-to-one matching while satisfying fairness among all the agentswith cardinal utilities.
After revision: The past few years have seen a surge of work on fairness in social choice literature. A major portion of this work has focused on allocation settings where items must be allocated fairly to agents with diverse preferences. In comparison, fairness in two-sided settings where agents have to be matched to other agents, with preferences on both sides, has received much less attention. In this paper, we explore the problem of finding a stable many-to-one matching while satisfying fairness among all the agentswith cardinal utilities.