FAI: Advancing Fairness in AI with Human-Algorithm Collaborations

人工智能（AI）系统越来越多地用于协助人类做出高风险决策，例如在线信息管理、简历筛选、抵押贷款、警察监控、公共资源分配和审前拘留。虽然希望算法的使用将改善社会结果和经济效率，但人们也提出了担忧，即算法系统可能会从历史数据中继承人类偏见，使对已经脆弱的人群的歧视永久化，并且通常无法体现特定社区的重要价值观。最近关于算法公平性的研究描述了不公平性在开发过程中不同阶段可能出现的方式，产生了许多关于公平性的定量概念，并提供了执行这些概念的方法。然而，在过度简化的算法目标和现实世界决策环境的复杂性之间存在着显著的差距。该项目旨在通过明确考虑实际利益相关者的特定环境公平原则，他们可接受的公平-效用权衡，以及人类决策者在整个算法系统开发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，该项目实现了密切的人机协作，将创新的机器学习方法与人机交互（HCI）方法相结合，以获取人类专家和利益相关者的反馈和偏好。有三个主要的研究活动，自然对应于人类在循环中的人工智能系统的三个阶段。首先，该项目将开发新的公平激发机制，使利益相关者能够有效地表达他们对公平的看法。为了超越传统的统计群体公平方法，研究者将基于诱导反馈制定新的个体公平度量。其次，该项目将开发算法和机制，以管理第一步开发的新公平指标与多个现有公平和准确性指标之间的权衡。最后，该项目将开发算法来检测和减轻人类操作员的偏见，以及在部署人工智能系统期间依靠人类反馈来纠正和消除现有模型偏见的方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.