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Online bipartite-matching platforms are ubiquitous and find applications inimportant areas such as crowdsourcing and ridesharing. In the most generalform, the platform consists of three entities: two sides to be matched and aplatform operator that decides the matching. The design of algorithms for suchplatforms has traditionally focused on the operator's (expected) profit. Sincefairness has become an important consideration that was ignored in the existingalgorithms a collection of online matching algorithms have been developed thatgive a fair treatment guarantee for one side of the market at the expense of adrop in the operator's profit. In this paper, we generalize the existing workto offer fair treatment guarantees to both sides of the market simultaneously,at a calculated worst case drop to operator profit. We consider group andindividual Rawlsian fairness criteria. Moreover, our algorithms havetheoretical guarantees and have adjustable parameters that can be tuned asdesired to balance the trade-off between the utilities of the three sides. Wealso derive hardness results that give clear upper bounds over the performanceof any algorithm.

Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual