III: Small: RUI: A Fairness Auditing Framework for Predictive Mobility Models

近年来，从带有GPS的设备以及基于位置的社交媒体收集的位置数据的使用有所增加。这种类型的位置数据可用于城市计算和城市规划环境中的各种决策目的。例如，在交通管理和人群流动的背景下，位置数据已被证明为了解和预测访问和拥堵模式提供了巨大的机会，从而帮助管理人员相应地规划资源。长期以来，位置数据的隐私问题一直受到研究界的关注。几十年来的研究一直在研究如何从位置数据中剥离敏感信息，以阻止重新识别个人身份。这些努力的成功带来了将基于历史位置数据的预测性和生成性模型整合到决策任务中的新机会。然而，出现的一个关键问题是，这些模型和分析在多大程度上代表了每个人，公平和公平。最终，本研究的目标是确保这些模型和基础数据的私密性和公平性。本项目旨在定义一套从个人和集体层面(即人群流动)位置数据的角度来审核位置预测和生成模型的公平性的方法和途径。在其核心，该项目将倡导一个新颖的框架，通过提供一套与移动性相关的域特定的公平性度量，来审计集中式和分布式系统中移动性跟踪和模型的公平性。该项目的技术目标是两个研究项目。第一个重点是建设基础设施、知识和方法，通过公平感知的生成性人工智能模型创建时空数据，这些模型具有包容性，可以导致公平和公平的政策规划。第二个重点是建立基础设施、知识和方法，通过提供一套新的公平感知算法来增加和评估位置隐私保护方法(LPPM)的公平性，这些算法将满足预测准确性、隐私和公平的目标。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，认为值得支持。

Recent years have seen an increase in the use of location data collected from devices with GPS as well as location-based social media. This type of location data can be used for various decision-making purposes in the context of urban computing and city planning. For instance, in the context of traffic management and crowd flow, location data has been shown to provide immense opportunities for understanding and predicting visitation and congestion patterns, thus helping managers to plan resources accordingly. For a long time, privacy concerns about location data have received attention from the research community. Decades of research have been examining how to strip sensitive information from location data to block the re-identification of individuals. The success of these efforts has led to new opportunities for integrating predictive and generative models that are based on historical location data into decision-making tasks. However, a critical concern that arises is the extent to which such models and analyses are representative of everyone, fair, and equitable. Ultimately, the goal of this research is to ensure that such models and underlying data are both private and fair.This project aims to define a set of methods and approaches for auditing location predictive and generative models in terms of fairness from the perspectives of individual and collective level (i.e., crowd flow) location data. At its core, this project will advocate a novel framework for auditing the fairness of mobility traces and models in both centralized and distributed systems by offering a set of domain-specific fairness metrics related to mobility. The technical aim of this project is in two research thrusts. The first thrust focuses on building infrastructure, knowledge, and methods for the creation of spatial-temporal data through fair-aware generative AI models that are inclusive and can lead to fair and equitable policy planning. The second thrust focuses on building infrastructure, knowledge, and methods for increasing and evaluating the fairness of Location Privacy Preserving Methods (LPPMs) by offering a set of novel fair-aware algorithms that will satisfy the objectives of prediction accuracy, privacy, and 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.