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

AI-DCL：EAGER：城市预测分析中的偏见和歧视

基本信息

批准号：
1926470
负责人：
Constantine Kontokosta
金额：
$ 29.77万
依托单位：
New York University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926470&HistoricalAwards=false
关键词：
AI DCL EAGER Bias Discrimination

项目摘要

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.

城市使用公民生成的311份报告来确定服务需求，例如基础设施维修，啮齿动物侵扰，供暖中断和非法建筑物使用。由于公民报告提供了实时条件评估，因此城市机构分析这些数据以了解和预测问题和服务需求。但是，响应条件的公民报告并不统一。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获得电子政务系统而有所不同。也就是说，此类报告具有持续的空间，种族和经济不平等导致的系统偏见。因此，基于公民投诉数据的预测性城市分析可能会导致歧视性的城市政策，计划和决策以及对城市资源分配的不当分配，从而进一步加剧了有关邻里质量的偏见。该项目旨在通过建立统计机器学习模型来估计报告率偏见，以基于公民投诉（通过311个报告）提高城市分析的功效；向城市决策者，政策制定者和刨床提供工具，以形象化偏见的空间和社会经济依赖性；并正确纠正回应投诉的偏见 - - 导致更多的资源分配。该项目涉及三个与相关的目标：（1）分析通过311系统投诉的社会空间差异（2）了解社会经济，人口统计学因素和文化因素和投诉行为，以及（3）的社会关系，以及（3）的关系，以及（3），（3）报告率和问题的潜在严重程度。为此，研究人员开发了一个新的方法论框架，整合了多个数据源，并结合了机器学习和经济学的方法，以评估，量化和纠正报告偏见。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年至2017年在纽约市和堪萨斯城每年在纽约市和堪萨斯城每年在纽约市和堪萨斯城的每年超过8,000,000个地位的311个报告，从2017年到2017覆盖堪萨斯城的整个城市。这些数据集将与详细的建筑物和财产数据，社会经济和人口统计数据以及社区组织，社会基础设施和政治参与的衡量标准集成。 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问题，以及不足和报告的可能性。该项目的见解将构成识别，评估和计算公民自我报告数据中偏见的基础，并产生变革性的结果，可以通过利用预测分析和人工智能来为城市服务的有效且公平提供。通过对公民生成的数据的质量进行建模和改善，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，同时确保了这种参与所产生的数据具有代表性，可靠和有用的有用性，这反映了NSF的法定任务和范围的范围。