A Framework for Fairness in Two-Sided Marketplaces
This addresses fairness for users and providers in large-scale applications such as job recommendations, though it appears incremental by extending prior work.
The paper tackles fairness in two-sided marketplaces like search and recommender systems by proposing a definition and an end-to-end optimization framework that handles constraints from both sides and dynamic aspects, with simulations demonstrating its efficacy.
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.