A Framework for Fairness in Two-Sided Marketplaces

TL;DR

This paper introduces a scalable, flexible framework for ensuring fairness in two-sided marketplaces, addressing both source and destination sides, with demonstrated effectiveness through simulations.

Contribution

It develops an end-to-end optimization framework that incorporates diverse fairness constraints in large-scale two-sided marketplace systems.

Findings

Framework effectively enforces fairness constraints

Adaptable to various fairness definitions

Successful simulation results demonstrate efficacy

Abstract

Many interesting problems in the Internet industry can be framed as a two-sided marketplace problem. Examples include search applications and recommender systems showing people, jobs, movies, products, restaurants, etc. Incorporating fairness while building such systems is crucial and can have a deep social and economic impact (applications include job recommendations, recruiters searching for candidates, etc.). In this paper, we propose a definition and develop an end-to-end framework for achieving fairness while building such machine learning systems at scale. We extend prior work to develop an optimization framework that can tackle fairness constraints from both the source and destination sides of the marketplace, as well as dynamic aspects of the problem. The framework is flexible enough to adapt to different definitions of fairness and can be implemented in very large-scale settings. We perform simulations to show the efficacy of our approach.