CRII: AF: RUI: Algorithmic Fairness for Computational Social Choice Models

计算机现在支持多种偏好聚合/投票系统，甚至使新的系统成为可能，推动了对计算社会选择的深入研究。例如，在线流动民主平台允许用户直接就某个话题投票，或将投票委托给他们认为更知情并能代表他们利益的可信代理人。这些系统允许企业就从产品设计到公司自助餐厅提供的食物等问题做出集体决定。然而，最近对机器学习和人工智能系统中使用的算法的审计告诉我们，计算机做出的算法决策有可能无意中使个人或群体处于不利地位。这种算法偏见和歧视可以通过设计具有特定公平性保证的算法来抵消。这个项目将通过算法公平透镜研究计算社会选择，并阐明实现困难或冲突的公平概念的理论局限性。除了推进有益于社会和研究界的知识外，本项目中研究的主题将用于丰富计算机科学课程各个层次的课程，使其具有现实世界的应用，并将与更广泛的计算机科学教育界分享这些主题的课程。最后，调查员将指导计算机科学传统上代表性不足的群体的本科生，使研究生院的管道多样化。算法通常用于实现现有的和建议的偏好聚合/投票系统，以及分析它们。与此同时，算法偏见和歧视已经在从招聘到医学再到刑事司法的广泛应用中被记录下来。在许多这些领域，研究界已经通过形式化公平性的计算定义和设计明确提供公平性保证的算法做出了回应，特别是对于分类或推荐等机器学习任务。从高层面上讲，该项目旨在将计算社会选择与最近对自动化系统中算法公平性和公平性、问责制和透明度（FAccT）的更广泛研究结合起来。对计算机科学和其他学科的主要贡献将是：（1）制定新的计算问题，目标和约束，用于实现和评估投票系统，这些系统可以指导未来基于特定现实世界应用的算法公平性工作;（2）设计和分析这些问题的算法，可以提供公平性保证;（3）证明不可能的结果，确定在这些设置中哪些公平概念是相互不相容的或难以解决的。（1）的重点将是在算法公平性的基础理论工作与计算社会选择的特定应用领域之间建立联系。（3）的工作将呼应算法公平和社会选择理论领域的开创性不可能结果。因此，（1）和（3）将告知研究者自己的工作（2），但也提出了新的问题，以研究community.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。

Computers now support many kinds of preference aggregation/voting systems and have even made new ones possible, driving deeper study into computational social choice. For example, online liquid democracy platforms allow users to vote directly on a topic or delegate their vote to a trusted proxy whom they believe is more informed and will represent their interests. These systems have allowed businesses to make collective decisions on issues ranging from product design to food offered in the corporate cafeteria. However, recent audits of algorithms used in machine learning and artificial intelligence systems have taught us that algorithmic decisions made by computers have the potential to unintentionally disadvantage individuals or groups of people. Such algorithmic bias and discrimination can be countered by designing algorithms with specific fairness guarantees built in. This project will investigate computational social choice through an algorithmic fairness lens and illuminate the theoretical limitations of achieving difficult or conflicting concepts of fairness. In addition to advancing knowledge that benefits society and the research community, topics studied in this project will be used to enrich courses at every level of the computer science curriculum with engaging real-world applications, and lessons featuring these topics will be shared with the broader computer science education community. Finally, the investigator will mentor undergraduate students from traditionally underrepresented groups in computer science, diversifying the pipeline to graduate school. Algorithms are commonly used to implement existing and proposed preference aggregation/voting systems as well as to analyze them. At the same time, algorithmic bias and discrimination has been documented in a broad range of applications from hiring to medicine to criminal justice. In many of these areas, the research community has responded by formalizing computational definitions of fairness and designing algorithms that explicitly offer fairness guarantees, especially for machine learning tasks such as classification or recommendation. At a high level, this project seeks to unite computational social choice and the recent research into algorithmic fairness and fairness, accountability, and transparency (FAccT) in automated systems more broadly. The main contributions to computer science and other disciplines will be: (1) Formulating new computational problems, objectives, and constraints for implementing and evaluating voting systems that can guide future work in algorithmic fairness that is grounded in a specific real-world application; (2) Designing and analyzing algorithms for these problems that can provide fairness guarantees; and (3) Proving impossibility results that establish which notions of fairness in these settings are incompatible with each other or intractable. A focus of (1) will be to build connections between foundational, theoretical work in algorithmic fairness and specific application areas in computational social choice. The work of (3) will echo the seminal impossibility results in the areas of both algorithmic fairness and social choice theory. Thus, (1) and (3) will inform the investigator’s own work on (2), but also pose new problems to the research community.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.