Subgroup fairness in two-sided markets

It is well known that two-sided markets are unfair in a number of ways. For example, female drivers on ride-hailing platforms 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 equalization 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. Although 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 semidefinite programming, thanks to its hidden convexity. 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.

Automated summary

This article proposes a new market-clearing mechanism for two-sided markets, which promotes income equality per hour worked across different subgroups and within each subgroup. It introduces the concept of subgroup fairness (Inter-fairness) combined with other fairness notions (Intra-fairness) and customer utility (Customer-Care) in the market-clearing problem. The study shows that the non-convex problem can be approximated efficiently using semidefinite programming, allowing for the implementation of the market-clearing mechanism.