CRII: RI: Fairness and Profitability in Online Matching Markets

匹配市场涉及不同的代理人(通常来自双方)，他们为了互惠互利而配对。在过去的十年中，配对市场通过互联网媒介出现并激增。它们已经演变成一种新的风格，称为在线配对市场(OMMS)，例子包括拼车、众包和互联网广告。OMS的特点是智能体的动态到达和实时决策需求。在拼车方面，据报道，从司机到骑手，都存在广泛的基于性别和种族的歧视。特别是，已经表明，来自少数群体的乘客比其他人更频繁地收到来自司机的不公平取消。在空间众包方面，报告提到，工人们试图避开来自社会经济不利地区的任务。该项目旨在利用强大的算法工具来减轻歧视，并为包括拼车和众包在内的各种现实世界的OMM增加收入。这项研究将涵盖以下三个方向。首先，该项目将努力仔细重新制定拼车方面的配套政策，使其有利于容易受到歧视的弱势群体。第二，这个项目将寻求定量研究各种实际OMMS实现的公平和收入之间的内在关系。这两个目标--高度公平和高收入--可能会相互冲突。该项目将开发多目标优化模型，以研究盈利能力和股权之间的权衡。第三，该项目旨在利用在线员工的到达模式来增加众包收入。众包中两个突出的挑战性问题是员工的动态到达和他们未知的任务技能。该项目将利用机器学习技术来学习在线工作者的到达模式，并使用基于在线匹配的模型来收获这些模式。这项拟议的研究可能会对社会产生重大的、更广泛的影响，因为它可能有助于减少拼车和众包方面的歧视。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Matching markets involve heterogeneous agents (typically from two parties) who are paired for mutual benefits. During the last decade, matching markets have emerged and proliferated through the medium of the Internet. They have evolved into a new style, called Online Matching Markets (OMMs), with examples including ridesharing, crowdsourcing, and Internet advertising. OMMs feature dynamic arrivals of agents and the real-time decision-making requirement. For ridesharing, a wide range of gender- and race-based discrimination has been reported from drivers to riders. In particular, it has been shown that riders from minority groups have received unfair cancellations from drivers much more frequently than others. In spatial crowdsourcing, reports mention that workers tried to avoid tasks from socioeconomically disadvantaged places. This project aims to leverage powerful algorithmic tools to mitigate discrimination and promote revenue for a large variety of real-world OMMs, including ridesharing and crowdsourcing. The research will cover the following three directions. First, the project will try to carefully re-craft the matching policy in ridesharing such that it is favorable for disadvantaged groups who are vulnerable to discrimination. Second, this project will seek to quantitatively study the inherent relationship between fairness and revenue achieved by various practical OMMs. The two objectives--high fairness and high revenue--can be somewhat conflicting with each other. The project will develop multi-objective optimization models to investigate the tradeoff between profitability and equity. Third, this project aims to leverage the arrival patterns of online workers to increase revenue in crowdsourcing. Two prominent challenging issues in crowdsourcing are workers' dynamic arrivals and their unknown skills for tasks. The project will utilize machine-learning techniques to learn the arrival patterns of online workers and use online-matching based models to harvest those patterns. The proposed research could have significant broader impacts on society as it can potentially contribute to reducing discrimination in ridesharing and crowdsourcing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.