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。在城市计算和城市规划的背景下，这种类型的位置数据可用于各种决策目的。例如，在交通管理和人群流动的背景下，已显示位置数据为理解和预测探访和拥塞模式提供了巨大的机会，从而帮助管理人员相应地计划资源。长期以来，有关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基于历史位置数据的预测和生成模型整合到决策任务中带来了新的机会。但是，出现的关键问题是这些模型和分析代表所有人公平和公平的程度。最终，这项研究的目的是确保此类模型和基础数据既私密又公平。该项目旨在根据个人和集体水平的角度（即人群流）位置数据来定义一组审核位置预测和生成模型的方法和方法。本项目的核心将提倡一个新颖的框架，用于通过提供与移动性相关的一组特定领域的公平度量指标来审核集中式和分布式系统中的移动性轨迹和模型的公平性。该项目的技术目的是两个研究推力。第一个推力重点是建立基础架构，知识和方法，用于通过包容性的公平生成的AI模型来创建空间 - 周期性数据，这些模型具有包容性，可以导致公平，公平的政策计划。第二个力量的重点是建立基础架构，知识和方法，用于增加和评估位置隐私保留方法（LPPM）的公平性，通过提供一系列新颖的公平意识算法，以满足预测准确性，隐私和公平性的目标，以反映NSF的智力和构建范围的范围。审查标准。