论文标题
在双面市场上公平的框架
A Framework for Fairness in Two-Sided Marketplaces
论文作者
论文摘要
互联网行业中许多有趣的问题都可以作为双面市场问题。示例包括搜索应用程序和推荐系统，显示人员，工作，电影，产品，餐馆等。在建立此类系统时结合公平性至关重要，可能会产生深厚的社会和经济影响（申请包括工作建议，寻找候选人的招聘人员等）。在本文中，我们提出了一个定义，并开发了一个端到端的框架，以在大规模构建此类机器学习系统时实现公平。我们扩展了先前的工作，以开发一个优化框架，该框架可以应对市场来源和目的地方面的公平限制以及问题的动态方面。该框架足够灵活，可以适应公平的不同定义，并且可以在非常大规模的设置中实现。我们执行模拟以显示我们方法的功效。
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.
