CRII: III: Rethinking Fairness: Fairness as a Survival Analysis

在机器学习社区内外，人们越来越担心人工智能（AI）面临偏见和歧视危机，迫切需要AI系统纳入公平性约束。美国国会已经认识到这一问题，并一直试图通过《公民问责法案》。它要求对自动化系统的准确性、公平性、偏见、歧视、隐私和安全性进行评估，并要求公司纠正他们在此过程中发现的任何问题。大多数现有的公平性评估工作假设记录的可用性，在这些记录中，源数据被注释了应用公平性定义和公平性算法所需的类别。然而，这种假设在现实世界的各种社会敏感应用中是不切实际的，从精准医学到营销分析，精算分析和累犯预测工具。因此，迫切需要研究实验室中“公平”模型的设计与其在真实的世界中的部署之间的差距所产生的问题。为此，本项目将重新审视公平的基本定义，并揭示现有公平文献中的特质，这些特质来自于假设在实践中无法获得的信息。接下来，该项目将旨在弥合当前人工智能公平性研究与其现实世界部署之间的差距，从而提高对人工智能社会影响的理解，并显著减少其潜在的社会歧视。为了实现这一目标，该项目将制定一个新的公平性作为生存分析问题，其中类标签的可用性并不总是得到保证，但仍然有一个要求，即类似的个人受到类似的对待。第一个研究目标是从两个不同的角度来量化缺失标签下的个体不公平。具体地说，第一个跟踪将公平性视为输入和输出空间中相似性的相关性，这使得能够定义可用于统计审查数据的公平性度量。第二个定义将构成另一个公平性问题，从鲁棒性的角度来看，评估是否相似的个人遭受不同程度的预测稳定性。第二个研究目标将进行初步调查，共同解决偏差减少和统计审查管理模型的建立，以确保效用最大化，同时最大限度地减少个人之间的偏见。这些标准将被制定为联合优化的正则化项，并且不要求所有个体都具有类标签。该项目的成果预计将包括多功能的工件，以确保在各种现实世界的社会敏感的应用程序的公平性保证。此外，该项目将引入新的任务设置，为未来人工智能公平性的实际应用研究铺平道路。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。

There has been increasing concern within the machine learning community and beyond that Artificial Intelligence (AI) faces a bias and discrimination crisis, urgently requiring AI systems to incorporate fairness constraints. The US Congress has recognized this issue and has been trying to pass the Algorithmic Accountability Act. It demands systems be evaluated for “accuracy, fairness, bias, discrimination, privacy and security within automated systems and companies would be required to correct any issues they uncovered during the process.” Most existing work on evaluating fairness assumes the availability of records in which the source data is annotated with categories needed to apply the fairness definition and fairness algorithm at hand. This assumption, however, is impractical in a diversity of real-world, socially-sensitive applications, ranging from precision medicine to marketing analytics, actuarial analysis and recidivism prediction instruments. There is thus a critical need to study the problem that arises from the gap between the design of a “fair” model in the lab and its deployment in the real world. To this end, this project will revisit the foundational definitions of fairness and reveal idiosyncrasies in the existing fairness literature stemming from assuming information that is not available in practice. Next, this project will aim to bridge the gap between current AI fairness studies and their real-world deployment, leading to improved understanding of the societal impact of AI and significant reduction in its potential for social discrimination. To achieve this goal, the project will formulate a new fairness-as-a-survival-analysis problem, where the availability of class labels is not always guaranteed, but there is still a requirement that similar individuals are treated similarly. The first research objective focuses on quantifying individual unfairness in the presence of missing labels from two different perspectives. Specifically, the first track will see fairness as the correlation of similarity in the input and output spaces, which enables defining a fairness measure usable on statistically censored data. The second definition will constitute another fairness issue arising from the perspective of robustness, evaluating whether similar individuals suffer dissimilar levels of prediction stability. The second research objective will make an initial investigation jointly addressing bias reduction and statistical censoring management in model building, so as to ensure utility maximization while minimizing bias across individuals. These criteria will be formulated as regularization terms for joint optimization and will not require all individuals to have a class label. The outcomes of this project are expected to include versatile artifacts that ensure fairness guarantees in various real-world socially-sensitive applications. Furthermore, the project will introduce a new task setting, paving the way for future research in the practical application of AI fairness.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.