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：智慧城市运行于计算预测框架之上，这些框架收集、聚合并利用大规模传感器网络的数据。然而，这些框架容易受到数据和算法偏见的多种来源影响，常常导致不公平的预测结果。本文中，我们首先通过研究田纳西州查塔努加的真实城市数据，证明偏见在微观层面存在于时间和空间上。为缓解此类偏见问题，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进行公平的城市策略调整与生成。Fairguard框架包含两个阶段：首先，我们开发了一个静态生成器，通过最小化选定属性之间的相关性，基于时间逻辑条件减少数据偏见；然后，为确保预测算法的公平性，我们设计了一个动态组件，通过利用逻辑规则调节预测结果并生成未来公平预测。评估显示，启用逻辑的静态Fairguard能有效减少有偏相关性，而动态Fairguard能在运行时保证受保护群体的公平性，同时对整体性能影响极小。