FAI: AI Algorithms for Fair Auctions, Pricing, and Marketing

该项目开发了在人工智能中介的拍卖、定价和营销中做出公平决策的算法，从而促进了国家繁荣和经济福利。由于直接访问消费者数据、实施个性化的能力以及实时运行算法的能力，人工智能系统在商业环境中的部署蓬勃发展。例如，用户看到的广告是个性化的，因为广告商愿意在广告展示拍卖中出价更高，以接触到具有特定人口统计特征的用户。拼车平台的定价决定或贷款利率都是根据消费者的特征定制的，以实现利润最大化。社交媒体平台上的营销活动针对的是用户，因为他们能够预测他们将能够影响他们的社交网络中的哪些人。不幸的是，这些应用程序显示出歧视。住房和就业广告拍卖中的歧视性目标，贷款和叫车服务的歧视性定价，以及将某些受保护群体排除在外的营销活动对社交网络用户的不同待遇都已曝光。该项目将开发理论框架和人工智能算法，以确保来自受保护群体的消费者在这些设置中不会受到有害的歧视。新的算法将促进在这些应用程序中公平地开展业务。该项目还支持召开会议，将从业者、政策制定者和学者聚集在一起，讨论将公平的人工智能算法整合到法律和实践中。该项目开发了新的理论框架，根据拍卖、定价和营销这三个典型商业领域的公平和商业目标来分析算法。该方法考虑了决策渠道的三个方面。首先，该项目旨在了解确保公平拍卖、定价和营销所需的新型标准，并设计新的算法，将这些公平标准纳入真实世界的大型系统。其次，对于这些业务环境中的每一个，项目都会考虑如何收集数据，以便在下游决策任务中获得公平的结果。第三，该项目考虑纳入或不纳入公平措施，从长远来看如何对公司和消费者产生积极或消极的影响，特别是在存在竞争的情况下。该奖项反映了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。

This project develops algorithms for making fair decisions in AI-mediated auctions, pricing, and marketing, thus advancing national prosperity and economic welfare. The deployment of AI systems in business settings has thrived due to direct access to consumer data, the capability to implement personalization, and the ability to run algorithms in real-time. For example, advertisements users see are personalized since advertisers are willing to bid more in ad display auctions to reach users with particular demographic features. Pricing decisions on ride-sharing platforms or interest rates on loans are customized to the consumer's characteristics in order to maximize profit. Marketing campaigns on social media platforms target users based on the ability to predict who they will be able to influence in their social network. Unfortunately, these applications exhibit discrimination. Discriminatory targeting in housing and job ad auctions, discriminatory pricing for loans and ride-hailing services, and disparate treatment of social network users by marketing campaigns to exclude certain protected groups have been exposed. This project will develop theoretical frameworks and AI algorithms that ensure consumers from protected groups are not harmfully discriminated against in these settings. The new algorithms will facilitate fair conduct of business in these applications. The project also supports conferences that bring together practitioners, policymakers, and academics to discuss the integration of fair AI algorithms into law and practice. The project develops novel theoretical frameworks to analyze algorithms according to both fairness and business objectives for three canonical business domains: auctions, pricing, and marketing. The approach considers three aspects of the decision-making pipeline. First, the project aims to understand the new types of criteria required to ensure fair auctions, pricing, and marketing, and designs novel algorithms that can incorporate these fairness criteria in real-world large-scale systems. Second, for each of these business contexts, the project considers how data can and should be collected in order to induce fair outcomes in the downstream decision-making task. Thirdly, the project considers how incorporating fairness measures, or failing to do so, can positively or negatively affect firms and consumers in the long-term, particularly in the presence of competition.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.