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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.

城市使用市民生成的311报告来确定服务需求，如基础设施维修、啮齿动物感染、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，公民对条件的报告并不统一;相反，报告频率因社会经济和人口群体、文化差异、对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，这种报告数据带有系统性偏见，这是由于持续的空间、种族和经济不平等造成的。因此，基于市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，以及城市资源的错误分配，进一步强化对社区质量的偏见。该项目旨在提高基于市民投诉的城市分析的效率（通过311份报告）通过建立统计机器学习模型来估计报告率偏差;为城市决策者、政策制定者和规划者提供工具，以可视化偏差的空间和社会经济依赖性;以及纠正处理投诉时的偏颇，使资源分配更公平。这项计划包括三个互相关连的目标：（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，人口统计和文化因素与投诉行为之间的关系，（3）为城市机构提供一种方法来解释观察到的报告偏见，报告率和潜在问题的严重性。为此，研究人员开发了一个新的方法框架，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（NYC 311）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年纽约市和堪萨斯城每年超过8，000，000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）、邻里状况评估，以及2014年至2017年覆盖整个堪萨斯城的21,046份个人回复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。项目产出包括：（1）基于人口统计学、社会经济学、文化和邻里因素评估公民报告概率的模型，（2）按邻里估计报告不足和过度行为并对自我报告数据进行加权以用于解释观察到的偏差的模型训练的模型，以及（3）交互式可视化工具，以帮助城市管理者、社区组织、以及公众了解投诉举报的空间模式、举报问题的性质，以及举报不足和过度举报的可能性。该项目的见解将成为识别、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、公平地提供城市服务。该项目通过建立公民生成数据的模型并提高其质量，为增加公民参与提供了方法基础（例如在治理、公民科学和协作知识生产方面），同时确保这种参与产生的数据具有代表性、可靠性，该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准。