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.Comment: This paper was accepted by the 8th ACM/IEEE Conference on Internet of
Things Design and Implementatio