research-article

Fairguard: Harness Logic-based Fairness Rules in Smart Cities

Authors:

Ayan Mukhopadhyay,

Meiyi MaAuthors Info & Claims

IoTDI '23: Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation

Pages 105 - 116

https://doi.org/10.1145/3576842.3582371

Published: 09 May 2023 Publication History

Abstract

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 biased correlations while dynamic Fairguard can guarantee fairness on protected groups at run-time with minimal impact on overall performance.

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Cited By

Zhao TTaherisadr MElmalaki S(2024)FAIRO: Fairness-aware Sequential Decision Making for Human-in-the-Loop CPS2024 ACM/IEEE 15th International Conference on Cyber-Physical Systems (ICCPS)10.1109/ICCPS61052.2024.00015(87-98)Online publication date: 13-May-2024
https://doi.org/10.1109/ICCPS61052.2024.00015
An ZWang XT. Johnson TSprinkle JMa M(2023)Runtime Monitoring of Accidents in Driving Recordings with Multi-type Logic in Empirical ModelsRuntime Verification10.1007/978-3-031-44267-4_21(376-388)Online publication date: 3-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-44267-4_21

Index Terms

Fairguard: Harness Logic-based Fairness Rules in Smart Cities

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cover image ACM Conferences

IoTDI '23: Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation

May 2023

514 pages

ISBN:9798400700378

DOI:10.1145/3576842

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 09 May 2023

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IoTDI '23: International Conference on Internet-of-Things Design and Implementation

May 9 - 12, 2023

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Cited By

Zhao TTaherisadr MElmalaki S(2024)FAIRO: Fairness-aware Sequential Decision Making for Human-in-the-Loop CPS2024 ACM/IEEE 15th International Conference on Cyber-Physical Systems (ICCPS)10.1109/ICCPS61052.2024.00015(87-98)Online publication date: 13-May-2024
https://doi.org/10.1109/ICCPS61052.2024.00015
An ZWang XT. Johnson TSprinkle JMa M(2023)Runtime Monitoring of Accidents in Driving Recordings with Multi-type Logic in Empirical ModelsRuntime Verification10.1007/978-3-031-44267-4_21(376-388)Online publication date: 3-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-44267-4_21

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