FAI: Addressing the 3D Challenges for Data-Driven Fairness: Deficiency, Dynamics, and Disagreement

数据驱动的决策系统越来越多地用于指导重要决策，从贷款审批到学校招生再到监狱量刑指南。用于提供这些决策的现有方法通常被专门设计为最大化单个标准（例如，准确性、实用性）。对于具有社会影响力的应用程序，这种方法忽略了大多数其他重要的决策考虑因素。 此外，目前的大多数方法都假设决策是一次性做出的，或者独立于以前的类似决策，决策所需的所有信息都是可用的，并且可以用一个公平的标准来描述决策。毫不奇怪，这些假设在真实的世界中并不成立。该项目调查了芝加哥市的教育，公共卫生和城市振兴决策应用，以及与Wild Me合作的环境政策应用，Wild Me是一个保护非营利的人工智能。该项目旨在开发一种公平的机器学习方法，该方法考虑到信息不足，动态决策以及对单一公平标准的分歧。 该项目旨在开发一种基于鲁棒估计的公平机器学习方法，以解决应用领域中常见的三种形式的复杂性：信息不足，动态决策和对单一公平标准的分歧。它通过推广代理的概念和学习潜在的群体成员结构作为一个迁移学习任务来解决信息不足问题。它灵活地将公平的概念扩展到具有重复交互的动态设置中，通过将其定义为个体的状态而不是决策者的行为。最后，它通过合理化观察决策的公平标准和平衡不同标准来解决公平性分歧。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而被认为值得支持。

Data-driven decision making systems are increasingly used to guide important decisions ranging from loan approvals to school admissions to prison sentencing guidelines. Existing methods for providing these decisions are often exclusively designed to maximize a single criterion (e.g., accuracy, utility). For socially-impactful applications, such an approach ignores most of the other important decision considerations. Moreover, most of the current methods assume that decisions are made once or are independent of previous similar decisions, that all the information necessary for the decision is available, and that a single criterion of fairness can characterize the decision. Not surprisingly, these assumptions do not hold in the real world. This project investigates education, public health, and urban revitalization decision-making applications in the City of Chicago, as well as environmental policy applications in collaboration with Wild Me, an AI for conservation non-profit. The project aims to develop a fair machine learning approach that takes into account deficiency of information, dynamic decision making, and disagreement about a single fairness criterion. This project aims to develop a fair machine learning approach based on robust estimation to address three forms of complexity common in application domains: deficiency of information, dynamic decisions, and disagreement about a single fairness criterion. It approaches information deficiency by generalizing the notion of proxies and learning latent group membership structure as a transfer learning task. It flexibly extends the notion of fairness to dynamic settings with repeated interactions by defining it over states of the individuals rather than decision maker actions. Finally, it addresses fairness disagreements by rationalizing the fairness criteria of observed decisions and balancing different criteria.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.