SCC-PG: Privacy and Fairness in Planning when using Third-Party, Heterogeneous Data Sources

今天的城市和社区规划能够从积极和被动参与数据收集的居民产生的丰富的流动性和位置数据中获得信息。移动性信息可以从多个来源收集，这些信息通常由第三方收集和维护，以用于多个异类应用程序。然而，当结合在一起时，这些第三方的、不同种类的数据可以描绘出城市景观中丰富的运动图景，这可能是社区规划者的重要信息来源。然而，天真地包含不同的数据源，例如通过机器学习，带来了一些技术和社会挑战，影响了城市居民的隐私，并引发了围绕公平和社会正义的重要问题。这些数据在社区之间的分布并不均匀，可能会遗漏重要的人群，并对算法决策产生偏见，而且高度详细的位置信息可能会泄露本应受到保护的居民信息。作为这一规划拨款的一部分，该项目将解决与隐私和公平相关的基础研究挑战，在社区规划中应用不同的数据，同时考虑到居民的参与和规划者的决策。包括：(1)我们如何识别和减轻社区规划中可能产生的来自不同第三方数据源的潜在偏见？(2)我们如何设计隐私感知算法，以合并不同和不完整的数据，同时为社区规划提供有意义的结果？(3)我们如何将公平和偏见的原则与隐私保护数据分析方法结合起来？(4)我们如何识别数据源中的代表性不足并探索有助于参与的因素？(5)居民和社区规划者如何理解与移动数据相关的隐私？此外，该项目将寻求通过社区研讨会在规划拨款过程中扩大社区伙伴关系和外联，以确定基础研究的关键方面以及如何将成果整合到实践中。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，被认为值得支持。

Civic and community planning today is able to draw from a wealth of mobility and location data generated by residents who both actively and passively participate in data collection. Mobility information can be gleaned from a number of sources, which are often collected and maintained by third-parties for uses in multiple, heterogeneous applications. However, when combined, these third-party, heterogeneous data can paint a rich picture of movement through the urban landscape, that could be an important source of information for community planners. However, the naive inclusion of heterogeneous data sources, such as through machine learning, presents a number of technical and social challenges that impact the privacy of city residents and raise important questions around fairness and social justice. The data is not uniformly distributed across communities, potentially leaving out important populations and biasing algorithmic decision making, and the highly detailed location information may leak information about residents that should be protected. As part of this planning grant, this project will address basic research challenges associated with privacy as well as fairness in the application of heterogeneous data for community planning that take into account both resident participation and planner decision making. Including: (1) How do we both identify and mitigate potential biases in community planning that can arise from heterogeneous, third-party data sources? (2) How do we design privacy-aware algorithms that can merge disparate and incomplete data while providing meaningful results for community planning? (3) How do we combine principles of fairness and bias with privacy preserving data analysis methods? (4) How do we identify under-representation within data sources and explore factors that contribute to participation? (5) How do residents and community-planners understand privacy with respect to mobility data? Additionally, the project will seek to expand community partnership and outreach during the planning-grant process through community workshops to identify key aspects of the basic research and how the outcomes can be integrated into practice.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.