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

应急管理人员需要获得正确的数据，以便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日益相关的数据源，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。一种补救办法是将负责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、文本和对话，这些图像、文本和对话反映了他们的社区。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，本项目研究的根本问题是利用社区应急响应小组（CERT）的当地成员的力量，利用人工智能（即人类-人工智能团队）来弥合这一差距的可行性。这个项目的独特之处在于利用与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，利用基于人工智能的系统将CERT志愿者整合到社交媒体数据的自动化处理中。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-人工智能团队方面培训CERT志愿者，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志愿者，这些志愿者利用对建筑环境的理解来梳理社交媒体，帮助机器发现数据中的新模式。因此，该项目支持NSF的使命，即通过展示利用当地CERT志愿者与应急管理人员合作产生灾害情况意识的价值，促进科学进步，促进国家健康、繁荣和福利。该计划拨款的目标是分析现有的人类-人工智能团队灾难数据，并让焦点小组的公民合作伙伴参与，以更好地了解CERT志愿者、应急管理人员、关键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，我们在技术空间和人在环协议中提出了关于志愿者团队的理论。该项目为更多公民参与灾害规划和响应提供了有意义的途径，并为CERT志愿者开发了培训课程，这些志愿者利用社交媒体数据努力建立可持续的志愿者工作。这个项目是响应轨道B -公民创新挑战-自然灾害复原力与国家科学基金会和国土安全部的合作。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.