Subgroup Fairness in Two-Sided Markets

TL;DR

This paper introduces a novel market-clearing mechanism for two-sided markets that promotes subgroup fairness, balancing pay across diverse groups while maintaining efficiency, demonstrated through ride assignment simulations.

Contribution

It proposes a new non-linear fairness-based market-clearing model with an efficient approximation method using semi-definite programming.

Findings

Effective promotion of subgroup fairness in ride-sharing markets.

Scalable approximation method for non-convex market-clearing problems.

Trade-offs between inter- and intra-fairness demonstrated.

Abstract

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.