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.

翻译：在线二分匹配平台无处不在，在众包和网约车等重要领域具有应用。在最一般的形式中，平台由三个实体组成：待匹配的两方以及决定匹配结果的平台运营方。这类平台算法的设计传统上侧重于运营方的（预期）利润。由于公平性已成为一个被现有算法忽视的重要考量，一系列在线匹配算法已被开发出来，这些算法以牺牲运营方利润为代价，为市场一方提供公平待遇保障。在本文中，我们推广了现有工作，以在运营方利润的最坏情况计算损失下，同时为市场双方提供公平待遇保障。我们考虑了群体和个体的罗尔斯公平准则。此外，我们的算法具有理论保障，并包含可调参数，能够根据需要灵活调整以平衡三方效用之间的权衡。我们还推导了困难性结果，为任何算法的性能给出了明确的上界。