Bias in smart city governance: How socio-spatial disparities in 311 complaint behavior impact the fairness of data-driven decisions
Bias in smart city governance: How socio-spatial disparities in 311 complaint behavior impact the fairness of data-driven decisions
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DOI：
10.1016/j.scs.2020.102503
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发表时间：
2021-01-01
影响因子：
11.7
通讯作者：
Hong, Boyeong
中科院分区：
文献类型：
--
作者：
Kontokosta, Constantine E.;Hong, Boyeong
“智慧”城市的治理和决策越来越依赖居民报告的数据和数据驱动的方法，以提高城市运营和规划的效率。然而，这些数据中的偏差问题和智慧城市结果的公平性问题受到的关注相对有限。这是一个令人不安的重大遗漏，因为社会公平应该是智慧城市的一个重要方面，需要在使用新技术和数据工具时加以解决和考虑。本文通过分析密苏里州堪萨斯城居民“311”投诉行为的社会空间差异来检验居民报告数据中的偏见。我们利用来自详细的311报告和全面的居民满意度调查的数据，并在空间上将这些数据与违反法规的行为，社区特征和街道状况评估相结合。我们引入了一个模型来识别居民与政府互动的差异，并根据投诉行为对报告不足和过度报告的社区进行分类。尽管有更大的客观和主观需求，低收入和少数民族社区不太可能报告街道状况或“滋扰”问题，而优先考虑更严重的问题。我们的研究结果构成了承认和解释自我报告数据中的数据偏差的基础，并通过偏差感知数据驱动的流程，为更公平地提供城市服务做出了贡献。
Governance and decision-making in "smart" cities increasingly rely on resident-reported data and data-driven methods to improve the efficiency of city operations and planning. However, the issue of bias in these data and the fairness of outcomes in smart cities has received relatively limited attention. This is a troubling and significant omission, as social equity should be a critical aspect of smart cities and needs to be addressed and accounted for in the use of new technologies and data tools. This paper examines bias in resident-reported data by analyzing socio-spatial disparities in '311' complaint behavior in Kansas City, Missouri. We utilize data from detailed 311 reports and a comprehensive resident satisfaction survey, and spatially join these data with code enforcement violations, neighborhood characteristics, and street condition assessments. We introduce a model to identify disparities in resident-government interactions and classify underand over-reporting neighborhoods based on complaint behavior. Despite greater objective and subjective need, low-income and minority neighborhoods are less likely to report street condition or "nuisance" issues, while prioritizing more serious problems. Our findings form the basis for acknowledging and accounting for data bias in self-reported data, and contribute to the more equitable delivery of city services through bias-aware data-driven processes.