It is well known that two-sided markets are unfair in a number of ways. For instance, female workers at Uber earn less than their male colleagues per mile driven. Similar observations have been made for other minority subgroups in other two-sided markets. Here, we suggest a novel market-clearing mechanism for two-sided markets, which promotes equalisation of the pay per hour worked across multiple subgroups, as well as within each subgroup. In the process, we introduce a novel notion of subgroup fairness (which we call Inter-fairness), which can be combined with other notions of fairness within each subgroup (called Intra-fairness), and the utility for the customers (Customer-Care) in the objective of the market-clearing problem. While the novel non-linear terms in the objective complicate market clearing by making the problem non-convex, we show that a certain non-convex augmented Lagrangian relaxation can be approximated to any precision in time polynomial in the number of market participants using semi-definite programming. This makes it possible to implement the market-clearing mechanism efficiently. On the example of driver-ride assignment in an Uber-like system, we demonstrate the efficacy and scalability of the approach, and trade-offs between Inter- and Intra-fairness.

翻译：众所周知，双边市场在多个方面存在不公平现象。例如，Uber的女性司机每英里收入低于男性同事。在其他双边市场中，其他少数子群也发现了类似现象。本文提出了一种针对双边市场的新型市场出清机制，该机制既能促进不同子群间每小时劳动报酬的均等化，也能实现子群内部的均等化。在此过程中，我们引入了一个新的子群公平性概念（称为“群体间公平性”），该概念可与各子群内部的公平性概念（称为“群体内公平性”）以及客户效用（客户关怀）相结合，共同作为市场出清问题的目标函数。尽管目标函数中引入的非线性项使问题非凸，增加了市场出清的复杂性，但我们证明：通过半定规划方法，可在参与者数量多项式时间内将某种非凸增广拉格朗日松弛逼近至任意精度。这使得市场出清机制能够高效实施。以优步式系统中的司机-订单匹配为例，我们验证了该方法的有效性和可扩展性，并展示了群体间公平性与群体内公平性之间的权衡关系。