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