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，以便有效和高效地规划和应对灾难。社交媒体提供了一个与灾难管理日益相关的数据源，但应急组织通常缺乏大规模监控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，以实现可扩展的监控。让当地志愿者参与这一过程非常重要，因为他们具有独特的能力，能够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将这些人类志愿者/人工智能系统与应急管理组织系统配对。因此，该项目调查的根本问题是利用社区应急小组(CERT)当地成员的优势与人工智能-称为人-AI团队-来弥合这一差距的可行性。该项目独特的民间方面是利用与CERT组织的现有合作来评估可行性。其长期愿景是开发一个可持续、可复制和经验丰富的框架，将CERT志愿者整合到使用基于人工智能的系统的社交媒体数据自动处理中。该项目通过向不同的学生提供研究经验，以及在社交媒体和人工智能团队方面培训CERT志愿者，来支持教育和多样性。这些发现可以帮助应急经理更好地培训志愿者，这些志愿者通过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，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难情况意识的价值，促进科学进步，促进国家的健康、繁荣和福利。这笔规划赠款的目标是分析现有的人类-人工智能团队灾难数据，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、关键政府组织和非政府组织的态度和信念。该项目将加深对数字志愿者团队的深入了解，了解他们如何工作，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，我们提出了关于技术空间中的志愿者团队和人在环中协议的理论。该项目为更多公民参与灾难规划和应对提供了有意义的方式，并为使用社交媒体数据的CERT志愿者开发了培训课程，以努力建立可持续的志愿者努力。该项目是对与NSF和国土安全部合作的Track B-公民创新挑战-对自然灾害的复原力的回应。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。

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.