New research paper explores fairness in gig economy

It is well known that two-sided markets, where buyers and sellers interact through a platform, are unfair in various ways. For instance, female drivers on ride-hailing platforms like Uber earn less than their male counterparts per mile driven. Similar observations have been made for other minority subgroups in other two-sided markets.

In their research, Quan Zhou, Robert Shorten and Jakub Mareček propose a novel market-clearing mechanism for two-sided markets that promotes equalization of pay per hour worked across multiple subgroups as well as within each subgroup. In doing so, the authors introduce a novel notion of subgroup fairness called Inter-fairness, which can be combined with other notions of fairness within each subgroup (called Intra-fairness) and the utility for the customers in the objective of the market-clearing problem. 

As part of the research, the authors have explored the trade-off between Intra-group and Inter-group fairness using the Yellow Taxi Trip Dataset as an example of a ride-hailing platform. They have shown that considering both objectives simultaneously is possible within the market-clearing and leads to an efficiently approximable non-convex augmented Lagrangian formulation. The implementation is available at Fairness-in-Two-Sided-Markets (Github repository). 

The notion, insights, and algorithms may be applicable across a range of two-sided markets, such as online labor platforms and college admissions, and could possibly be extended to a comprehensive framework for multiple fairness criteria. This work could also contribute to a theoretical foundation for the study of fairness in two-sided markets.