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

CRII：III：重新思考公平：公平作为生存分析

• 批准号: 2245895
• 负责人: Wenbin Zhang
• 金额: $ 17.5万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245895&HistoricalAwards=false
• 关键词: CRII; III; Rethinking; Fairness; Survival

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.

机器学习界内外越来越担心人工智能（AI）面临偏见和歧视危机，迫切要求人工智能系统纳入公平性约束，美国国会已经认识到这一问题，并一直试图通过算法问责。它要求对系统进行“准确性、公平性、偏见、歧视、隐私和安全性评估”，并且要求公司纠正在此过程中发现的任何问题。其中源数据是然而，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“公平”模型的设计与其在现实世界中的部署之间的差距所产生的问题。为此，该项目将重新审视公平的基本定义并揭示这一问题。特质接下来，该项目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和重大社会影响的理解。为了实现这一目标，该项目将制定一个新的公平作为生存分析问题，其中类别标签的可用性并不总是得到保证，但仍然要求相似的个体。第一个研究目标侧重于量化个体。具体来说，第一条定义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同行评审数据的公平性度量。从稳健性角度评估所产生的公平性问题，即相似的个体是否会遭受不同程度的预测稳定性。第二个研究目标将共同解决模型构建中的偏差减少和统计审查管理问题，以确保效用最大化，同时最小化偏差。这些标准将被制定为联合优化的正则化术语，并且不需要所有个人都有类别标签，预计该项目的结果将包括确保各种现实世界社会敏感应用程序的公平性保证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。