AF:RI:Small: Fairness in allocation and machine learning problems: algorithms and solution concepts

公平是人类社会的最高追求之一。随着先进的计算机技术、巨大的计算能力和数据的可用性，转向计算机辅助决策是自然和不可避免的。公平计算面临着双重挑战：(1)通过计算实现公平，即公平地分配资源(或任务)，例如，疫苗分配和分担污染成本；(2)基于机器学习的决策产生的计算公平，例如司法决定和医疗决定。围绕经济学和社会选择理论中的基本解决方案概念，该项目将显著推进这两个挑战的最新进展。在这个过程中，它将开发具有社会重要性的公平解决方案，并在经济学、机器学习、运筹学、社会选择理论和理论计算机科学的交叉点上建立理论。此外，拟议的教育活动将为从高中到研究生的所有层次的学生创造许多独特的研究机会，为社会培养高质量的研究人员和实践者。该项目将(I)解决关于经典公平解的存在和计算的最重要的公开问题，(Ii)通过将机器学习任务与丰富的社会选择理论文献相结合，为机器学习任务开发基于偏好的公平概念。前者可能在该领域一些最神秘的悬而未决的问题上提供根本突破。同时，后者可能为另一种更细微的公平机器学习理论铺平道路，该理论植根于社会选择理论的经典概念。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。

Fairness is one of the highest pursuits of human society. With advanced computer technology, enormous computational power, and data availability, it is natural and inevitable to turn to computer-aided decision-making. There arises a two-fold challenge of fair computation: (i) achieve fairness through computation, i.e., allocating resources (or tasks) fairly, for example, in vaccine distribution and sharing pollution costs, and (ii) fairness in computation arising from machine learning-based decision making, for example, justice decisions and deciding medical treatment. Centering around the fundamental solution concepts from economics and social choice theory, this project will significantly advance the state-of-the-art on both challenges. In the process, it will develop fair solutions of societal importance and build theory at the intersection of economics, machine learning, operations research, social choice theory, and theoretical computer science. In addition, the proposed educational activities will create many unique research opportunities for students at all levels, from high school to graduate students, generating high-quality researchers and practitioners for society.This project will (i) tackle the most important open problems on the existence and computation of classical fair solutions and (ii) develop preference-based fairness concepts for the machine learning tasks by marrying them to the rich literature on social choice theory. The former may provide radical breakthroughs on some of the most enigmatic open questions in the field. At the same time, the latter may pave the way for an alternate theory of fair machine learning that is more nuanced and is rooted in classical concepts from social choice theory.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.