Making Data Fair Through Optimal Trimmed Matching

Gordaliza, Paula; Inouzhe, Hristo

doi:10.1007/978-3-031-15509-3_26

Cited by 1 publication

(1 citation statement)

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“…On the other hand, there is vast research on specific problems in two-sided markets, ranging from matching between different types of users [31][32][33], market equilibrium [34], price discrimination issues [34][35][36], to fair ranking or recommendation systems [37][38][39][40]. Especially, considering users' gender preference, [36] propose a "female-only" subsystem that matches safety-concerned female riders to female drivers.…”

Section: Related Workmentioning

confidence: 99%

Subgroup fairness in two-sided markets

2023

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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.

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