LaLone, N., Natta, J. V., Cormier, M. V., Fraune, M. R., Hamilton, B., Dugas, P. O. T., et al. (2023). Flying SD Cards, Aerial Repeaters, & Homebrew Apps: Emergent Use of Technologies for Collaboration in Search and Rescue. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 1014–1032). Omaha, USA: University of Nebraska at Omaha.
Abstract: Search and rescue (SAR) teams are the first to respond to emergencies. This could include finding lost hikers, shoring buildings, or aiding people post-disaster. SAR combines orienteering, engineering, field medicine, and communication. Technology use in SAR has been changing with the proliferation of information communication technologies; so, we ask, how are established and emerging technologies used in SAR? Understanding how responders are adopting and adapting these technologies during SAR missions can inform future design and improve outcomes for SAR teams. We interviewed SAR volunteers to contextualize their experiences with technology and triangulated with additional questionnaire data. We discuss how technology use in SAR requires an intersection of expert knowledge and creative problem solving to overcome challenges in the field. This research contributes an understanding of the constraints on and implications for future SAR technologies and SAR operators’ creativity in emergent situations.
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Borglund, E., A.M., & Granholm, M. (2023). Challenges in work procedures in distributed crisis management. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 732–737). Omaha, USA: University of Nebraska at Omaha.
Abstract: This is a work in progress paper on work and IT usage in distributed crisis management. The data presented in this paper has been collected at a one-day tabletop exercise with four Swedish municipalities. Four members of the four municipalities’ crisis organizations were invited to the exercise, which was designed as one scenario divided into two cases. At the start of each case of the exercise, each municipality was split into two separate rooms, to simulate a distributed crisis management. During the first case they could communicate using phone, TETRA radio, and the Internet. During case two in the scenario, there was no Internet connection. The study indicates that all the municipalities managed to organize and solve the given tasks using primarily voice communication, in case one using phone or, e.g., Teams, and in case two using TETRA radio. Information sharing using IT was non-existing.
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Grace, R., Montarnal, A., Petitdemange, E., Rutter, J., Rodriguez, G. R., & Potts, M. (2023). Collaborative Information Seeking during a 911 Call Surge: A Case Study. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 649–662). Omaha, USA: University of Nebraska at Omaha.
Abstract: This case study examines collaborative information seeking in a public-safety answering point during a 911 call surge that occurred when a man fired an assault rifle at police officers and evaded capture for nearly an hour in March 2020. Overwhelmed by questionable and imprecise reports from 911 callers, telecommunicators and on scene responders began working together to conduct broad and deep searches for the shooter. Whereas broad searches improved the scope of information gathering by identifying multiple, albeit questionable and imprecise, reports of the suspect’s location, deep searches improved the quality of information gathering by investigating 911 callers’ reports using drone, helicopter, and patrol units. These findings suggest requirements for collaborative information seeking in public-safety answering points, including capabilities to conduct broad and deep searches using next-generation 911 technologies, and command and control requirements for triaging these search tasks within inter-organizational emergency response systems.
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Rustenberg, K., Radianti, J., & Gjøsæter, T. (2023). Exploring Demons for the Establishment of Team Situational Awareness. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 636–648). Omaha, USA: University of Nebraska at Omaha.
Abstract: Individual situational awareness (SA) is crucial for building team SA, which is necessary for achieving a shared understanding of a situation, making informed decisions, and taking appropriate actions. This article examines the communication barriers that emerge when transitioning from individual to team SA in emergency management scenarios. We observed two emergency exercises on “ongoing life-threatening violence” and dam failure causing hospital congestion. The study was complemented with interviews with participants of these exercises, aiming at identifying barriers called SA-demons in the team setting. We discovered barriers that hinder the establishment of team SA, including a vicious cycle of mistrust, a fragmented information trap, a false feeling of mastery trap, and a decaying memory trap. These barriers can stem from individual, organizational, or technological factors. To complement existing SA theories, we applied the Cynefin framework and found that standard operating procedures can be potential barriers when transitioning into chaotic or complex domains.
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Cruz, J. A. dela, Hendrickx, I., & Larson, M. (2023). Towards XAI for Information Extraction on Online Media Data for Disaster Risk Management. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 478–486). Omaha, USA: University of Nebraska at Omaha.
Abstract: Disaster risk management practitioners have the responsibility to make decisions at every phase of the disaster risk management cycle: mitigation, preparedness, response and recovery. The decisions they make affect human life. In this paper, we consider the current state of the use of AI in information extraction (IE) for disaster risk management (DRM), which makes it possible to leverage disaster information in social media. We consolidate the challenges and concerns of using AI for DRM into three main areas: limitations of DRM data, limitations of AI modeling and DRM domain-specific concerns, i.e., bias, privacy and security, transparency and accountability, and hype and inflated expectations. Then, we present a systematic discussion of how explainable AI (XAI) can address the challenges and concerns of using AI for IE in DRM.
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