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

III：小：RUI：预测移动模型的公平性审核框架

• 批准号: 2304213
• 负责人: Afra Mashhadi
• 金额: $ 55.35万
• 依托单位: University of Washington
• 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: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304213&HistoricalAwards=false
• 关键词: III; Small; RUI; Fairness; Auditing

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.

近年来，从具有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背景下的各种决策目的。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理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，关于位置数据的隐私问题一直受到研究界的关注。数十年的研究一直在研究如何从位置数据中剥离敏感信息，以阻止个人的重新识别。这些努力的成功为将基于历史位置数据的预测和生成模型集成到决策任务中带来了新的机会。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，公平和公正。最终，本研究的目标是确保这些模型和基础数据既私密又公平。本项目旨在从个人和集体层面（即，人群流）位置数据。在其核心，该项目将倡导一种新的框架，通过提供一组与移动性相关的特定于域的公平性度量，来审计集中式和分布式系统中的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重点。第一个重点是建立基础设施、知识和方法，通过具有公平意识的生成式人工智能模型创建时空数据，这些模型具有包容性，可以导致公平和公正的政策规划。第二个重点是建立基础设施，知识和方法，通过提供一组新颖的公平感知算法来提高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，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进行评估，被认为值得支持审查标准。