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 \textit{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：摘要：智慧城市依赖于计算预测框架，这些框架从大规模传感器网络中收集、聚合并利用数据。然而，此类框架易受数据源偏差与算法偏差的多重影响，往往导致不公平的预测结果。本研究首先通过分析美国田纳西州查塔努加市的实际城市数据，证明偏差在微观层面同时存在时间性与空间性。为缓解此类偏差问题，本文提出Fairguard——一种基于微观时间逻辑的公平性城市政策调整与生成方法，适用于复杂时空域。Fairguard框架包含两个阶段：首先，我们开发静态生成器，通过最小化选定属性间的相关性，基于时间逻辑条件减少数据偏差；其次，为确保预测算法的公平性，我们设计动态组件，通过运用逻辑规则调节预测结果并生成未来的公平预测。评估表明：启用逻辑的静态Fairguard可有效降低偏差相关性，而动态Fairguard能在运行时保障受保护群体的公平性，且对整体性能影响极小。