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

FAI：用于公平拍卖、定价和营销的人工智能算法

• 批准号: 2147361
• 负责人: Adam Elmachtoub
• 金额: $ 39.3万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2147361&HistoricalAwards=false
• 关键词: FAI; AI; Algorithms; Fair; Auctions

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.

该项目开发了在AI介导的拍卖，定价和营销中做出公正决策的算法，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，AI系统在业务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竞标更多，以吸引具有特定人口统计特征的用户。为了最大程度地提高利润，对乘车共享平台或贷款利率的定价决定是根据消费者的特征进行定制的。社交媒体平台上的营销活动基于预测他们将能够在社交网络中影响谁的能力。 不幸的是，这些应用显示出歧视。 住房和工作广告拍卖中的歧视性定位，贷款和乘车服务的歧视定价以及通过营销活动对社交网络用户的不同处理，以排除某些受保护的群体。该项目将开发理论框架和AI算法，以确保在这些环境中不会受到保护群体的消费者的歧视。新算法将促进这些应用程序中公平的业务行为。该项目还支持将实践者，政策制定者和学者聚集在一起的会议，以讨论公平AI算法与法律和实践的整合。该项目开发了新颖的理论框架，以根据三个规范业务领域的公平和业务目标分析算法：拍卖，定价和营销。该方法考虑了决策管道的三个方面。首先，该项目旨在了解确保公平拍卖，定价和营销所需的新类型标准，并设计新颖的算法，这些算法可以将这些公平标准纳入现实世界中的大型系统。其次，对于这些业务环境中的每一个，该项目都考虑如何收集数据，以便在下游决策任务中引起公平的成果。第三，该项目考虑如何将公平措施或未能做到这一点，从长期来看，可能会对公司和消费者产生积极或负面影响。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来通过评估来获得支持的。

项目成果

Nonstationary Dual Averaging and Online Fair Allocation

非平稳对偶平均和在线公平分配

• 发表时间: 2022
• 期刊: 36th Conference on Neural Information Processing Systems (NeurIPS 2022
• 作者: Liao, Luofeng;Gao, Yuan;Kroer, Christian
• 通讯作者: Kroer, Christian

Solving optimization problems with Blackwell approachability

• DOI: 10.1287/moor.2023.1376
• 发表时间: 2022-02
• 期刊: ArXiv
• 作者: Julien Grand-Clément;Christian Kroer

The Power of Greedy for Online Minimum Cost Matching on the Line

• DOI: 10.1145/3580507.3597794
• 发表时间: 2022-10
• 期刊: Proceedings of the 24th ACM Conference on Economics and Computation
• 作者: Eric Balkanski;Yuri Faenza;Noémie Périvier

Implementing Fairness Constraints in Markets Using Taxes and Subsidies

利用税收和补贴在市场中实施公平约束

• DOI: 10.1145/3593013.3594051
• 发表时间: 2023
• 期刊: and Transparency
• 作者: Peysakhovich, Alexander;Kroer, Christian;Usunier, Nicolas
• 通讯作者: Usunier, Nicolas

STATISTICAL INFERENCE FOR FISHER MARKET EQUILIBRIUM

渔民市场均衡的统计推断

• 发表时间: 2023
• 期刊: The Eleventh International Conference on Learning Representations
• 作者: Liao, Luofeng;Gao, Yuan;Kroer, Christian
• 通讯作者: Kroer, Christian