CIVIC-PG Track B: Creating an AI-based Community-wide Efficient and Equitable Response System

随着越来越多的请求和城市政府和签约服务的全国劳动力短缺，对高效、易于访问和公平的响应系统的需求比以往任何时候都更加迫切。为了满足这一需求，这一大学与社区的合作旨在通过将最先进的人工智能技术应用于响应系统来彻底改变下一代紧急服务，这些技术将缩短紧急响应时间，同时提高它们的可及性和公平性。为此，本项目采取以下步骤：1)实现对非紧急请求的自动化反应，从而确保9-1-1调度员能够为可能受到紧急情况不利影响的人员提供快速援助；2)准确确定紧急反应时间，从而最大限度地提高站点和反应单位的安置效率，并有可能提高(保险服务)ISO评级，降低公民和企业的保险费率；3)提取现场信息，以支持紧急情况下急救者的行动，以挽救生命和时间；4)创建以社区为中心的方法，以确保公平和值得信赖的紧急和非紧急反应系统。这一规划赠款旨在找出现有紧急和非紧急响应系统中存在的低效、不公平和无法获得的根源，并通过开发新的基于人工智能的干预措施来补救这些问题。在这项规划工作中，由大学研究人员、公共安全专业人员和社区合作伙伴组成的项目团队正在与服务不足的社区成员合作，以确定响应系统中降低这些系统的效率、公平性和可及性的关键瓶颈。我们的愿景是利用人工智能和数据驱动学习的原则性技术来开发干预措施的原型，以解决这些瓶颈。该项目是对公民创新挑战计划的回应-轨道B-弥合基本资源与服务和社区需求之间的差距-是NSF、国土安全部和能源部的合作。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

With a growing number of requests and national labor shortages in city governments and contracted services, the need for efficient, easily accessible, and equitable response systems is more urgent than ever. To address this need, this university-community collaboration aims to revolutionize the next generation of emergency services by applying state-of-the-art AI techniques to response systems that will reduce emergency response times while increasing their accessibility and equity. To do so, this project takes the following steps: 1) automate responses to non-emergency requests and thus ensuring 9-1-1 dispatchers are available to provide quick assistance to those who may be adversely affected by emergencies; 2) precisely ascertain emergency response times, which can maximize the efficiency of the placement of stations and response units and potentially result in an improved (insurance services) ISO rating and lower insurance rates for the citizens and businesses, 3) extract on-scene information to support first-responders operations to save lives and time during emergencies, and 4) create a community-centric approach to ensure equitable and trustworthy emergency and non-emergency response systems. This planning grant aims to identify sources of inefficiency, inequity, and inaccessibility present in existing emergency and non-emergency response systems and remedy them by developing novel AI-based interventions. In this planning effort, the project team consisting of university researchers, public safety professionals, and community partners is collaborating with members of underserved communities to identify crucial bottlenecks in the response system that reduce efficiency, equity, and accessibility of these systems. The vision is to develop prototypes of interventions to address these bottlenecks using principled techniques from AI and data-driven learning.This project is in response to the Civic Innovation Challenge program—Track B. Bridging the gap between essential resources and services & community needs—and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy.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.