SCC-CIVIC-PG Track B: Assessing the Feasibility of Systematizing Human-AI Teaming to Improve Community Resilience

应急管理人员需要访问正确的数据，以有效和高效地规划和应对灾害。社交媒体提供了一个与灾害管理越来越相关的数据源，但应急响应组织通常缺乏大规模监测和挖掘社交媒体数据的能力。一种补救措施是将标记相关信息的人类志愿者与计算机配对，以训练和更新人工智能（AI）系统，以进行可扩展的监控。让当地志愿者参与这一过程非常重要，因为他们有独特的能力识别与当地相关的图像、文本和反映其社区的对话。 然而，我们目前还没有机制来系统地将这些人类志愿者/人工智能系统与应急管理组织配对。 因此，该项目调查的根本问题是利用社区应急响应小组（CERT）的本地成员的优势与AI的可行性-称为人类-AI团队-来弥合这一差距。 该项目独特的CIVIC方面是利用与CERT组织的现有合作来评估可行性。 长期愿景是开发一个可持续的，可复制的，经验丰富的框架，将CERT志愿者整合到使用基于AI的系统自动处理社交媒体数据中。 该项目通过为不同的学生提供研究经验，以及在社交媒体和人类人工智能团队中培训CERT志愿者来支持教育和多样性。 研究结果可以帮助应急管理人员更好地培训他们的志愿者，他们利用对建筑环境的理解梳理社交媒体，帮助机器看到数据中的新模式。 因此，该项目支持NSF的使命，以促进科学的进步和推进国家的健康，繁荣和福利，通过展示利用当地CERT志愿者的价值，与应急管理人员合作，以产生灾害情况意识。 这项计划拨款的目标是分析现有的人类-人工智能合作灾难数据，并让公民合作伙伴参与焦点小组，以更好地了解CERT志愿者、应急管理人员、主要政府组织和非政府组织的态度和信念。 该项目将深入了解数字志愿者团队，他们如何工作，如何激励他们，以及如何让他们支持应急管理人员的目标。因此，我们推进理论围绕志愿者团队在技术空间和人在环协议。 该项目为更多公民参与灾害规划和应对提供了有意义的途径，并为使用社交媒体数据的CERT志愿者开发培训课程，以建立可持续的志愿者工作。该项目是对Track B - CIVIC创新挑战赛的回应-与NSF和国土安全部合作的自然灾害恢复能力。该奖项反映了NSF的法定使命，并已被视为通过使用基金会的知识价值和更广泛的影响审查标准进行评估，

Emergency managers need access to the right data to effectively and efficiently plan for and respond to disasters. Social media offers a data source that is increasingly relevant for disaster management, but emergency response organizations typically lack capacity to monitor and mine social media data at scale. One remedy is to pair human volunteers, who label relevant information, with computers to train and update artificial intelligence (AI) systems for scalable monitoring. Including local volunteers in the process is important because they are uniquely capable of identifying locally-relevant images, text, and conversations that reflect their communities. Yet, we currently have no mechanism to systematically pair these human volunteer/AI-systems with emergency management organizations. Therefore, the fundamental issue this project investigates is the feasibility of leveraging the strengths of local members of a Community Emergency Response Team (CERT) with AI—called human-AI teaming—to bridge this gap. The unique CIVIC aspect of this project is to leverage existing collaborations with a CERT organization to assess the feasibility. The long-term vision is to develop a sustainable, replicable, and empirically informed framework for integrating CERT volunteers into the automated processing of social media data using an AI-based system. The project supports education and diversity by providing research experiences to diverse students, as well as training CERT volunteers in social media and human-AI teaming. Findings can help emergency managers better train their volunteers who comb through social media using understandings of the built environment to help machines see new patterns in data. Hence, this project supports NSF's mission to promote the progress of science and advance the nation's health, prosperity, and welfare by demonstrating the value of leveraging local CERT volunteers, in partnership with emergency managers, to generate disaster situation awareness. The goal of this planning grant is to analyze existing human-AI teaming disaster data and involve civic partners in focus groups to better understand the attitudes and beliefs of CERT volunteers, emergency managers, key governmental organizations, and non-governmental organizations. This project will develop deep knowledge of digital volunteer teams, how they work, how to motivate them, and how to have them support the objectives of emergency managers. Thus, we advance theory around volunteer teaming in the technology space and human-in-the-loop protocols. This project provides meaningful ways for more citizens to participate in disaster planning and response, and develops a training curriculum for CERT volunteers who work with social media data in an effort to build sustainable volunteer efforts.This project is in response to Track B - CIVIC Innovation Challenge - Resilience to Natural Disasters a collaboration with NSF and the Department of Homeland Security.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.