CRII: HCC: Practical Steps Toward Integrating the Tools of Emergency Management with Crisis Informatics Techniques

该项目旨在改进应急管理人员如何利用社会媒体帖子和遭受自然灾害和其他危机的公民创建的其他信息。这些信息有助于记录正在发生的危机并揭示需求；如果使用得当，这可以帮助应急管理人员更好地评估危机局势，并改进他们在何处以及如何应对的决策。然而，尽管最近在使用人工智能技术分析公民创造的信息的工具方面取得了进展，但这些工具尚未在实际的应急情况中得到有效部署。该项目的目标是通过分析应急响应人员的做法和培训，确定这些做法与现有信息分析工具的技术能力之间的差距，并通过使应急管理人员和工具开发人员在设计过程中更紧密地联系在一起，努力缩小差距，使有效部署更加可行。项目小组计划与应急管理组织密切合作，这种合作有可能挽救生命和财产，减少社区的痛苦和危机的影响。这项研究将分两个主要阶段进行。首先，项目团队将对应急管理从业人员的日常技术生活进行调查和访谈。通过评估各个领域（包括执法、消防科学、国土安全和紧急医疗服务）的应急管理技能，该小组将为其他研究人员提供有价值的背景，告诉他们在哪里可以找到利益相关者、合作者和发展空间。该小组还将收集和分析应急管理教学大纲，以了解如何教应急管理学生使用技术。在第二阶段，项目团队将与应急管理人员进行参与式设计练习，探索利用机器学习和信息检索的工具如何适应他们的实践，使用模拟现实分析精度水平的方法，以解释这些工具中不可避免的错误及其对人们在人在循环系统中工作能力的影响。在开发参与式设计材料的过程中，项目团队将评估TF-IDF、主题建模、上下文关键字等技术，以及它们所依赖的底层工具和数据集，评估这些技术在不同于它们所接受培训的环境中的安装、维护和应用程度。这两个阶段都将考虑许多情况，使研究小组能够评估不同信息来源、人口和不同类型灾难事件结果的一致性。该项目由以人为中心的计算和促进竞争研究的既定计划（EPSCoR）共同资助。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。

This project seeks to improve how emergency managers can make use of social media posts and other information created by citizens experiencing natural disasters and other crises. This information can help document unfolding crises and reveal needs; used well, this could help emergency managers better assess crisis situations and improve their decisions about where and how to respond. However, despite recent advances in tools that use artificial intelligence techniques to analyze citizen-created information, these tools have not been effectively deployed in actual emergency response situations. This project’s goal is to make effective deployment more feasible, through analyzing the practices and training of emergency responders, identifying gaps between those practices and the technical capabilities of existing information analysis tools, and working to close the gaps through bringing emergency managers and tool developers closer together in the design process. The project team plans to work closely with emergency management organizations, a collaboration that has the potential to save lives and property, reducing suffering and the impact of crises in communities. The research will proceed in two main phases. First, the project team will survey and interview emergency management practitioners about their everyday technological life. By evaluating the skills of emergency management across its respective domains (including law enforcement, fire science, homeland security, and emergency medical services), the team will provide valuable context to other researchers about where they can find stakeholders, collaborators, and space for development. The team will also collect and analyze emergency management syllabi in order to understand how emergency management students are taught to use technologies. In the second phase, the project team will conduct participatory design exercises with emergency managers that explore how tools that leverage machine learning and information retrieval could fit into their practices, using methods that simulate realistic levels of analysis accuracy in order to account for the inevitable presence of error in these tools and its effect on people’s ability to work in human-in-the-loop systems. In developing the participatory design materials, the project team will evaluate techniques like TF-IDF, Topic Modelling, Keyword-in-Context, and the underlying tools and datasets they depend on, in terms of how well they can be installed, maintained, and applied to contexts different than those they were trained on. Both phases will look at a number of contexts, allowing the research team to evaluate consistency of results across sources of information, populations, and disparate kinds of disaster events. This project is jointly funded by Human Centered Computing and the Established Program to Stimulate Competitive Research (EPSCoR).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.