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.

中文翻译：

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双边市场中的子群公平

众所周知，双边市场在很多方面都是不公平的。例如，优步的女工每行驶一英里的收入低于男同事。对其他双边市场的其他少数群体也进行了类似的观察。在这里，我们建议为双边市场建立一种新颖的市场出清机制，以促进跨多个子组以及每个子组内部的每小时工资的均等化。在这个过程中，我们引入了一个新的子组公平概念（我们称之为 Inter-fairness），它可以与每个子组内的其他公平概念（称为 Intra-fairness）和客户的效用（Customer-Care ) 以市场出清问题为目标。虽然目标中的新非线性项使问题变得非凸，从而使市场出清复杂化，我们表明，某个非凸增强拉格朗日松弛可以使用半定规划逼近市场参与者数量的任何时间多项式精度。这使得有效实施市场出清机制成为可能。在类似 Uber 的系统中的司机乘车分配示例中，我们展示了该方法的有效性和可扩展性，以及内部公平性和内部公平性之间的权衡。