Smart cities operate on computational predictive frameworks that collect, aggregate, and utilize data from large-scale sensor networks. However, these frameworks are prone to multiple sources of data and algorithmic bias, which often lead to unfair prediction results. In this work, we first demonstrate that bias persists at a micro-level both temporally and spatially by studying real city data from Chattanooga, TN. To alleviate the issue of such bias, we introduce Fairguard, a micro-level temporal logic-based approach for fair smart city policy adjustment and generation in complex temporal-spatial domains. The Fairguard framework consists of two phases: first, we develop a static generator that is able to reduce data bias based on temporal logic conditions by minimizing correlations between selected attributes. Then, to ensure fairness in predictive algorithms, we design a dynamic component to regulate prediction results and generate future fair predictions by harnessing logic rules. Evaluations show that logic-enabled static Fairguard can effectively reduce the biased correlations while dynamic Fairguard can guarantee fairness on protected groups at run-time with minimal impact on overall performance.

翻译：智慧城市依赖于计算预测框架来收集、整合和利用大规模传感器网络的数据。然而，这些框架容易受到多种数据和算法偏差源的影响，常常导致不公平的预测结果。本文首先通过研究田纳西州查塔努加市的真实城市数据，证明了偏差在微观层面存在时间和空间上的持续性。为缓解此类偏差问题，我们引入了Fairguard，一种基于微观时间逻辑的方法，用于在复杂时空域中实现智慧城市政策的公平调整与生成。Fairguard框架由两个阶段组成：首先，我们开发了一个静态生成器，能够基于时间逻辑条件通过最小化所选属性之间的相关性来减少数据偏差；然后，为确保预测算法的公平性，我们设计了一个动态组件，通过利用逻辑规则来调控预测结果并生成未来公平的预测。评估表明，启用逻辑的静态Fairguard能有效减少偏差相关性，而动态Fairguard能在运行时保证受保护群体的公平性，同时将整体性能的影响降至最低。