# Fairguard: Harness Logic-based Fairness Rules in Smart Cities

arXiv: https://arxiv.org/abs/2302.11137 · HTML: https://mlsanity.com/paper/2302.11137/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 22 Feb 2023.

## Builds on (most cited first, 16 of 16)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Adversarial Removal of Demographic Attributes from Text Data](https://mlsanity.com/paper/1808.06640/) (2018)
- [Fair Regression: Quantitative Definitions and Reduction-based Algorithms](https://mlsanity.com/paper/1905.12843/) (2019)
- [Fair Regression with Wasserstein Barycenters](https://mlsanity.com/paper/2006.07286/) (2020)
- [An Empirical Study of Rich Subgroup Fairness for Machine Learning](https://mlsanity.com/paper/1808.08166/) (2018)
- [Residual Unfairness in Fair Machine Learning from Prejudiced Data](https://mlsanity.com/paper/1806.02887/) (2018)
- [Sample Selection for Fair and Robust Training](https://mlsanity.com/paper/2110.14222/) (2021)
- [Hierarchical Planning for Resource Allocation in Emergency Response Systems](https://mlsanity.com/paper/2012.13300/) (2020)
- [CitySpec: An Intelligent Assistant System for Requirement Specification in Smart Cities](https://mlsanity.com/paper/2206.03132/) (2022)
- [A Distributed Fair Machine Learning Framework with Private Demographic Data Protection](https://mlsanity.com/paper/1909.08081/) (2019)
- [Metric Distortion of Social Choice Rules: Lower Bounds and Fairness Properties](https://mlsanity.com/paper/1612.02912/) (2016)
- [A Novel Spatial-Temporal Specification-Based Monitoring System for Smart Cities](https://mlsanity.com/paper/2104.04904/) (2021)
- [Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit](https://mlsanity.com/paper/2204.11992/) (2022)

## Abstract (arXiv)

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.