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

AF:RI:Small：分配公平性和机器学习问题：算法和解决方案概念

• 批准号: 2334461
• 负责人: Ruta Mehta
• 金额: $ 60万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334461&HistoricalAwards=false
• 关键词: AF; RI; Small; Fairness; allocation

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.

公平是人类社会的最高追求之一。借助高级计算机技术，巨大的计算能力和数据可用性，转向计算机辅助决策是自然而必然的。出现了公平计算的两倍挑战：（i）通过计算实现公平性，即公平地分配资源（或任务），例如，在疫苗分布和共享污染成本中，以及（ii）基于机器学习的决策制定产生的计算公平性，例如，正义决策和决定医疗治疗。该项目以经济学和社会选择理论的基本解决方案概念为中心，将在这两个挑战上大大推动最新的问题。在此过程中，它将在经济学，机器学习，运营研究，社会选择理论和理论计算机科学的交汇处开发出公平的社会重要性解决方案和建立理论。此外，拟议的教育活动将为从高中到研究生的各个级别的学生创造许多独特的研究机会，为社会提供高质量的研究人员和实践者。（i）将解决有关经典公平解决方案的存在和计算最重要的开放问题，并（ii）通过与他们竞争社交文学的基于机器学习任务的基于偏好的公平性概念，并为他们提供精心的选择。前者可能会在该领域最神秘的开放问题上提供激进的突破。同时，后者可能为公平机器学习的替代理论铺平了道路，该理论更加细微，并植根于社会选择理论的经典概念。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通过评估来进行评估的。