FAI: Advancing Fairness in AI with Human-Algorithm Collaborations

FAI：通过人类算法合作促进人工智能的公平性

• 批准号: 2125692
• 负责人: Zhiwei Steven Wu
• 金额: $ 56.5万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125692&HistoricalAwards=false
• 关键词: FAI; Advancing; Fairness; AI; Human

Artificial intelligence (AI) systems are increasingly used to assist humans in making high-stakes decisions, such as online information curation, resume screening, mortgage lending, police surveillance, public resource allocation, and pretrial detention. While the hope is that the use of algorithms will improve societal outcomes and economic efficiency, concerns have been raised that algorithmic systems might inherit human biases from historical data, perpetuate discrimination against already vulnerable populations, and generally fail to embody a given community's important values. Recent work on algorithmic fairness has characterized the manner in which unfairness can arise at different steps along the development pipeline, produced dozens of quantitative notions of fairness, and provided methods for enforcing these notions. However, there is a significant gap between the over-simplified algorithmic objectives and the complications of real-world decision-making contexts. This project aims to close the gap by explicitly accounting for the context-specific fairness principles of actual stakeholders, their acceptable fairness-utility trade-offs, and the cognitive strengths and limitations of human decision-makers throughout the development and deployment of the algorithmic system. To meet these goals, this project enables close human-algorithm collaborations that combine innovative machine learning methods with approaches from human-computer interaction (HCI) for eliciting feedback and preferences from human experts and stakeholders. There are three main research activities that naturally correspond to three stages of a human-in-the-loop AI system. First, the project will develop novel fairness elicitation mechanisms that will allow stakeholders to effectively express their perceptions on fairness. To go beyond the traditional approach of statistical group fairness, the investigators will formulate new fairness measures for individual fairness based on elicited feedback. Secondly, the project will develop algorithms and mechanisms to manage the trade-offs between the new fairness measures developed in the first step, and multiple existing fairness and accuracy measures. Finally, the project will develop algorithms to detect and mitigate human operators' biases, and methods that rely on human feedback to correct and de-bias existing models during the deployment of the AI system.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.

越来越多地使用人工智能（AI）系统来帮助人类做出高风险决策，例如在线信息策划，恢复筛查，抵押贷款，警察监视，公共资源分配和审前拘留。尽管希望使用算法将改善社会成果和经济效率，但人们担心算法系统可能会从历史数据中继承人类偏见，使人们对已经脆弱的人群的歧视永久化，并且通常没有体现一个给定社区的重要价值。关于算法公平性的最新工作表征了不公平沿着开发管道的不同步骤出现的方式，产生了数十种定量的公平概念，并提供了执行这些概念的方法。但是，过度简化的算法目标与现实决策环境的并发症之间存在显着差距。该项目的目的是通过明确考虑实际利益相关者的上下文特定公平原则，他们可接受的公平 - 私人权衡权衡以及人类决策者在整个算法系统的发展和部署过程中的认知优势和局限性，来缩小差距。为了实现这些目标，该项目可以仔细进行人类合作的合作，这些合作将创新的机器学习方法与人类计算机互动（HCI）（HCI）的方法相结合，以引起人类专家和利益相关者的反馈和偏好。有三种主要的研究活动自然对应于人类在循环AI系统的三个阶段。首先，该项目将开发新颖的公平启发机制，使利益相关者能够有效地表达他们对公平性的看法。为了超越统计集团公平的传统方法，研究人员将根据引起的反馈制定新的公平措施。其次，该项目将开发算法和机制，以管理第一步中开发的新公平措施与多种现有公平和准确性措施之间的权衡。最后，该项目将开发算法来检测和减轻人类运营商的偏见，以及在AI系统部署期间依靠人类反馈来纠正和偏见的现有模型的方法。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和广泛的影响来评估CREITERIA的评估。