A Framework for Fairness in Two-Sided Marketplaces

Understand

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.