AI-DCL: EAGER: Bias and Discrimination in City Predictive Analytics

市民生成的311报告被城市用来确定服务需求，如基础设施维修、鼠患、供暖中断和非法建筑使用。由于市民报告提供实时状况评估，城市机构分析这些数据以了解和预测问题和服务需求。然而，公民根据情况进行报告的情况并不统一；相反，报告频率因社会经济和人口群体、文化差异、政府信任的差异以及电子政务系统的使用情况而异。也就是说，由于持续存在的空间、种族和经济不平等，这种报告数据带有系统性的偏见。因此，基于公民投诉数据的预测性城市分析可能导致歧视性的城市政策、规划和决策，以及城市资源的不当分配，进一步加剧对社区质量的偏见。该项目旨在通过建立统计机器学习模型来估计报案率偏差，以提高基于公民投诉(通过311份报告)的城市分析的效率；为城市决策者、政策制定者和规划者提供工具，使偏见的空间和社会经济依赖可视化；这个项目涉及三个相互关联的目标：(1)通过311系统分析投诉倾向的社会空间差异，(2)了解社会经济、人口和文化因素与投诉行为之间的关系，以及(3)为城市机构提供一种方法，以说明观察到的报告偏差，包括报告率和潜在的问题严重程度。为此，研究人员开发了一种新的方法框架，整合了多个数据源，并结合了机器学习和经济学的方法，用于评估、量化和纠正报告偏差。利用与纽约市311(NYC311)和堪萨斯城绩效管理办公室(DataKC)的合作，研究团队将使用2012至2017年纽约市和堪萨斯城每年超过800万份地理位置311报告的数据、法规执行和建筑违规记录(作为验证数据)、社区状况评估，以及2014至2017年覆盖整个堪萨斯城的21,046个人的详细公民满意度调查。这些数据集将与详细的建筑和财产数据、社会经济和人口数据以及社区组织、社会基础设施和政治参与的衡量标准相结合。项目成果包括：(1)基于人口、社会经济、文化和社区因素评估公民举报概率的模型；(2)按社区估计少报和多报行为的模型；以及(3)交互式可视化工具，以帮助城市管理人员、社区组织和普通公众了解投诉举报的空间模式、所报告问题的性质以及少报和多报的可能性。该项目的洞察力将成为识别、评估和解释公民自我报告数据中的偏见的基础，并产生变革性的结果，通过利用预测分析和人工智能，有助于高效和公平地提供城市服务。通过模拟和改进公民生成数据的质量，该项目为增加公民参与(例如，在治理、公民科学和协作知识生产中)提供了方法论基础，同时确保这种参与产生的数据具有代表性、可靠性和实用性。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Citizen-generated 311 reports are used by cities to identify service needs such as infrastructure repair, rodent infestations, heating outages, and illegal building use. Because citizen reports provide real-time condition assessment, city agencies analyze these data to understand and forecast problems and service demands. However, citizen reporting in response to conditions is not uniform; instead reporting frequency varies by socioeconomic and demographic group, cultural difference, differences in government trust, and access to e-government systems. That is, such reporting data carry systematic biases resulting from persistent spatial, racial, and economic inequalities. Consequently, predictive urban analytics based on citizen complaint data can result in discriminatory urban policy, planning, and decision-making, and misallocation of city resources, further reinforcing biases about neighborhood quality. This project seeks to improve efficacy of urban analytics based on citizen complaints (through 311 reports) by building statistical machine learning models to estimate reporting rate biases; providing tools to city decision makers, policy makers, and planers to visualize the spatial and socio-economic dependence of biases; and correct for the biases in responding to complaints --- leading to more just resource allocation.This project involves three inter-related objectives: (1) to analyze the socio-spatial variance in the propensity to complain through the 311 system, (2) to understand the relationship between socioeconomic, demographic, and cultural factors and complaint behavior, and (3) to provide a methodology for city agencies to account for observed reporting biases, both in terms of reporting rate and potential severity of problems. To do so, the investigators develop a new methodological framework, integrating multiple data sources and incorporating approaches from machine learning and economics, for assessing, quantifying, and correcting reporting bias. Leveraging collaborations with New York City 311 (NYC311) and the Kansas City Office of Performance Management (DataKC), the research team will use data of more than 8,000,000 geo-located 311 reports annually in NYC and Kansas City from 2012 to 2017, code enforcement and building violation records (as validation data), neighborhood condition assessments, and a detailed citizen satisfaction survey of 21,046 individual responses from 2014 to 2017 covering all of Kansas City. These datasets will be integrated with detailed building and property data, socioeconomic and demographic data, and measures of community organization, social infrastructure, and political participation. Project outputs include: (1) a model to assess the probability of citizen reporting based on demographic, socioeconomic, cultural, and neighborhood factors, (2) a model to estimate under- and over-reporting behavior by neighborhood and to weight self-reported data for model training that accounts for observed biases, and (3) an interactive visualization tool to assist city managers, community organizations, and the general public in understanding spatial patterns of complaint reporting, the nature of reported problems, and the likelihood of under- and over-reporting. The insights of this project will form the basis for identifying, evaluating, and accounting for bias in citizen self-reported data, and produce transformative results that can contribute to the efficient and fair delivery of city services by leveraging predictive analytics and artificial intelligence. By modeling and improving the quality of citizen-generated data, the project provides a methodological basis for increasing citizens' participation (e.g. in governance, citizen science, and collaborative knowledge production) while ensuring that the data produced by such participation is representative, reliable, and useful.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.