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

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.