FAI: Advancing Fairness in AI with Human-Algorithm Collaborations

人工智能（AI）系统越来越多地用于帮助人类做出高风险的决策，例如在线信息管理、简历筛选、抵押贷款、警方监控、公共资源分配和审前拘留。虽然人们希望算法的使用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，使对本已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。算法公平性的最新研究表明，在开发过程的沿着不同阶段，不公平性可能会出现，产生了数十种量化的公平性概念，并提供了实施这些概念的方法。然而，在过于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巨大的差距。该项目旨在通过明确说明实际利益相关者的上下文特定的公平原则，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的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，该项目实现了人类算法的密切合作，将联合收割机创新的机器学习方法与人机交互（HCI）方法相结合，以获得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在环AI系统的三个阶段。首先，该项目将开发新的公平性启发机制，使利益相关者能够有效地表达他们对公平性的看法。为了超越传统的统计组公平性方法，研究人员将根据引发的反馈制定新的公平性措施。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性度量与多个现有公平性和准确性度量之间的权衡。最后，该项目将开发检测和减轻人类操作员偏差的算法，以及在人工智能系统部署过程中依赖人类反馈来纠正和消除现有模型偏差的方法。该奖项反映了NSF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.