Tsai, C. - H., Rayi, P., Kadire, S., Wang, Y. - F., Krafka, S., Zendejas, E., et al. (2023). Co-Design Disaster Management Chatbot with Indigenous Communities. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 1–12). Omaha, USA: University of Nebraska at Omaha.
Abstract: Indigenous communities are disproportionately impacted by rising disaster risk, climate change, and environmental degradation due to their close relationship with the environment and its resources. Unfortunately, gathering the necessary information or evidence to request or co-share sufficient funds can be challenging for indigenous people and their lands. This paper aims to co-design an AI-based chatbot with two tribes and investigate their perception and experience of using it in disaster reporting practices. The study was conducted in two stages. Firstly, we interviewed experienced first-line emergency managers and invited tribal members to an in-person design workshop. Secondly, based on qualitative analysis, we identified three themes of emergency communication, documentation, and user experience. Our findings support that indigenous communities favored the proposed Emergency Reporter chatbot solution. We further discussed how the proposed chatbot could empower the tribes in disaster management, preserve sovereignty, and seek support from other agencies.
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Peng Xia, Ji Ruan, Dave Parry, Jian Yu, & Sally Britnell. (2023). Enhancing Triage Training for Mass Casualty Incidents with Virtual Reality and Artificial Intelligence. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 68–76). Palmerston North, New Zealand: Massey Unversity.
Abstract: Mass casualty incidents (MCIs) occur with natural or man-made disasters. Training emergency staff for combating MCIs is essential, but the cost can be high as such incidents rarely occur, and a physical simulation is resource-intensive. Triage is a critical task in dealing with MCIs. In this paper, we propose to use Virtual Reality (VR) and Artificial Intelligence (AI) technologies to build a low-cost, high-efficient system for MCI triage training. Our system captures more comprehensive training data and utilizes state-of-the-art AI evaluation methods.
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Carole Adam, & Cédric Lauradoux. (2022). A Serious Game for Debating about the Use of Artificial Intelligence during the COVID-19 Pandemic. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 554–563). Tarbes, France.
Abstract: Crises always impose a difficult compromise between safety and liberty, and the COVID-19 pandemic is no different. Governments have enforced various sanitary restrictions to reduce virus spread. With the help of Artificial Intelligence (AI), the scale of surveillance has risen to unprecedented levels. However, these technologies entail many risks, from potential errors or biases, to their extended enforcement beyond the duration of the initial crisis. Citizens should be aware that these technologies are not infallible, and measure the consequences of errors, so as to make informed decisions about what they want to accept, and for how long. To this aim, we have designed a serious game in the form of a municipal debate between citizens of a virtual town. Some first test sessions helped us improve the game design, and provided proof of the interest of this game to trigger debates and raise awareness.
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Muhammad Imran, Firoj Alam, Umair Qazi, Steve Peterson, & Ferda Ofli. (2020). Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 761–773). Blacksburg, VA (USA): Virginia Tech.
Abstract: Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings. This work analyzes the usefulness of social media imagery content to perform rapid damage assessment during a real-world disaster. An automatic image processing system, which was activated in collaboration with a volunteer response organization, processed ~280K images to understand the extent of damage caused by the disaster. The system achieved an accuracy of 76% computed based on the feedback received from the domain experts who analyzed ~29K system-processed images during the disaster. An extensive error analysis reveals several insights and challenges faced by the system, which are vital for the research community to advance this line of research.
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Lennart Landsberg, David Ganske, Christopher Munschauer, & Ompe Aimé Mudimu. (2020). Using Existing Data to Support Operational Emergency Response in Germany – Current Use Cases, Opportunities and Challenges. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 406–415). Blacksburg, VA (USA): Virginia Tech.
Abstract: The availability of resources in the fire and ambulance services in Germany is facing a radical change. Demographic and social transition is reducing the availability of volunteer personnel, and increasing traffic congestion in cities is resulting in longer travel times for emergency vehicles. This paper presents the findings of the definition phase of a research project that addresses these changes. It shows the basic idea of how resilience of fire and ambulance services can be improved by analyzing operational data from past incidents using artificial intelligence (AI). The primary objective is the development of a decision support system for control center dispatchers, which ensures optimal use of available resources. As the result of the definition phase, this paper gives an overview of existing data, current as well as future use cases and also highlights risks and challenges that have to be considered.
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