1. Decoupling the unfairness propagation chain in crowd sensing and learning systems for spatio-temporal urban monitoring
- Author
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Gang Wang, Shijia Pan, and Susu Xu
- Subjects
Task (computing) ,Sensing data ,Chain (algebraic topology) ,Process (engineering) ,Computer science ,Distributed computing ,media_common.quotation_subject ,Fidelity ,Imputation (statistics) ,Decoupling (cosmology) ,Cascading effects ,media_common - Abstract
In smart cities, urban monitoring systems rely on advanced mobile sensing and learning technologies to track large-scale urban systems and provide efficient urban services in real time. However, the fidelity and amount of sensors deployed at different geo-communities are closely related to their socioeconomic conditions, demographics, and entrenched geographic patterns, causing inequal sensing opportunities across communities. The biased sensing data contain distorted spatio-temporal patterns of undersensed community, inducing unfairness in subsequent algorithmic prediction and decision-making. This work characterize this unfairness propagation chain of sensing - learning - decision-making process. We introduce the first formal mathematical definitions to quantify and decouple community-level unfairness induced by joint cascading effects of sensing inequality and algorithmic bias. Our real-world experiments with vehicular crowdsensing system in Cangzhou, China verifies that sensing inequality, especially community-level gap of sensor fidelity, result in large fairness gap in spatio-temporal data imputation task. Our preliminary results show that sensing inequality amplifies the algorithmic bias. This work is a critical first step in formally defining and understanding unfairness propagation in intelligent spatio-temporal urban monitoring system.
- Published
- 2021