What does my technology facilitate`? A toolbox to help researchers understand the societal impact of a technology in the context of disasters
DOI:
https://doi.org/10.26443/seismica.v3i1.1144Keywords:
Disaster Risk Reduction, Safety culture, emerging technologiesAbstract
Disaster risk is increasing globally. Emerging technologies – Artificial Intelligence, Internet of Things, and remote sensing – are becoming more important in supporting disaster risk reduction and enhancing safety culture. Despite their presumed benefits, most research focuses on their technological potential, whereas societal aspects are rarely reflected. Taking a societal perspective is vital to ensure that these technologies are developed and operated in ways that benefit societies’ resilience, comply with ethical standards, are inclusive, and address potential risks and challenges. Therefore, we were particularly interested in understanding how societal impacts can be considered and leveraged throughout the development process. Based on an explorative literature review, we developed a toolbox for professionals working on emerging technologies in disaster risk reduction. By applying a Delphi study with experts on AI in seismology, we iteratively adapted and tested the toolbox. The results show that there is a need for guided reflection in order to foster discussion on the societal impacts. They further indicate a gap in the common understanding that is crucial for developing inclusive technologies or defining regulations. Our toolbox was found to be useful for professionals in reflecting on their developments and making technologies societally relevant, thereby enhancing societies’ resilience.
References
Adeel, A., Gogate, M., Farooq, S., Ieracitano, C., Dashtipour, K., Larijani, H., & Hussain, A. (2018). A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management. In Geological Disaster Monitoring Based on Sensor Networks (pp. 57–66). Springer Singapore. https://doi.org/10.1007/978-981-13-0992-2_5
Aitsi-Selmi, A., Egawa, S., Sasaki, H., Wannous, C., & Murray, V. (2015). The Sendai Framework for Disaster Risk Reduction: Renewing the Global Commitment to People’s Resilience, Health, and Well-being. International Journal of Disaster Risk Science, 6(2), 164–176. https://doi.org/10.1007/s13753-015-0050-9
Alsalat, G. Y., El-Ramly, M., Aly, A., & Said, K. (2018). Detection of Mass Panic using Internet of Things and Machine Learning. International Journal of Advanced Computer Science and Applications, 9(5). https://doi.org/10.14569/ijacsa.2018.090542
Arabameri, A., Saha, S., Mukherjee, K., Blaschke, T., Chen, W., Ngo, P. T. T., & Band, S. S. (2020). In Modeling Spatial Flood using Novel Ensemble Artificial Intelligence Approaches in Northern Iran.
Arshad, B., Ogie, R., Barthelemy, J., Pradhan, B., Verstaevel, N., & Perez, P. (2019). Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review. Sensors, 19(22), 5012. https://doi.org/10.3390/s19225012
Banna, Md. H. A., Taher, K. A., Kaiser, M. S., Mahmud, M., Rahman, Md. S., Hosen, A. S. M. S., & Cho, G. H. (2020). Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges. IEEE Access, 8, 192880–192923. https://doi.org/10.1109/access.2020.3029859
Bello, O. M., & Aina, Y. A. (2014). Satellite Remote Sensing as a Tool in Disaster Management and Sustainable Development: Towards a Synergistic Approach. Procedia - Social and Behavioral Sciences, 120, 365–373. https://doi.org/10.1016/j.sbspro.2014.02.114
Beroza, G. C., Segou, M., & Mostafa Mousavi, S. (2021). Machine learning and earthquake forecasting—next steps. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-24952-6
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118x.2012.678878
Bushnaq, O. M., Chaaban, A., & Al-Naffouri, T. Y. (2021). The Role of UAV-IoT Networks in Future Wildfire Detection. IEEE Internet of Things Journal, 8(23), 16984–16999. https://doi.org/10.1109/jiot.2021.3077593
Cao, Y., Boruff, B. J., & McNeill, I. M. (2017). The smoke is rising but where is the fire? Exploring effective online map design for wildfire warnings. Natural Hazards, 88(3), 1473–1501. https://doi.org/10.1007/s11069-017-2929-9
Connell, B. R., Jones, M., Mace, R., Mueller, J., Mullik, A., Ostroff, E., Sanford, J., Steinfeld, E., Story, M., & Vanderheiden, G. (1997). The principles of universal design—Version 2.0—4/1/97. The center for universal design, N.C. State University.
Costache, R., & Tien Bui, D. (2019). Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania. Science of The Total Environment, 691, 1098–1118. https://doi.org/10.1016/j.scitotenv.2019.07.197
Crawford, K., & Finn, M. (2014). The limits of crisis data: analytical and ethical challenges of using social and mobile data to understand disasters. GeoJournal, 80(4), 491–502. https://doi.org/10.1007/s10708-014-9597-z
Crenshaw, K. (1991). Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color. Stanford Law Review, 43(6), 1241. https://doi.org/10.2307/1229039
Dalkey, N. C. (1972). The Delphi method: An experimental study of group opinion. In Studies in the quality of life: Delphi and decision-making. Lexington Books.
Dalkey, N. C., & Helmer, O. (1963). An experimental application of the delphi method to the use of experts. Management Science, 9(3), 458–467.
Dallo, I., Stauffacher, M., & Marti, M. (2022). Actionable and understandable? Evidence-based recommendations for the design of (multi-)hazard warning messages. International Journal of Disaster Risk Reduction, 74, 102917. https://doi.org/10.1016/j.ijdrr.2022.102917
Datta, A., Wu, D. J., Zhu, W., Cai, M., & Ellsworth, W. L. (2022). DeepShake: Shaking Intensity Prediction Using Deep Spatiotemporal RNNs for Earthquake Early Warning. Seismological Research Letters, 93(3), 1636–1649. https://doi.org/10.1785/0220210141
Dong, L. (2013). A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 15.
Essam, Y., Kumar, P., Ahmed, A. N., Murti, M. A., & El-Shafie, A. (2021). Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia. Soil Dynamics and Earthquake Engineering, 147, 106826. https://doi.org/10.1016/j.soildyn.2021.106826
European Commission. (2004). Project Cycle Management Guidelines: Vol. Volume 1.
Furquim, G., Filho, G., Jalali, R., Pessin, G., Pazzi, R., & Ueyama, J. (2018). How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT. Sensors, 18(3), 907. https://doi.org/10.3390/s18030907
Gao, I. (2016, November). Using the Social Network Internet of Things to Mitigate Public Mass Shootings. 2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC). https://doi.org/10.1109/cic.2016.073
Gevaert, C. M., Carman, M., Rosman, B., Georgiadou, Y., & Soden, R. (2021). Fairness and accountability of AI in disaster risk management: Opportunities and challenges. Patterns, 2(11), 100363. https://doi.org/10.1016/j.patter.2021.100363
Gjøsæter, T., Radianti, J., & Chen, W. (2020). Universal Design of ICT for Emergency Management from Stakeholders’ Perspective: A Systematic Literature Review. Information Systems Frontiers, 23(5), 1213–1225. https://doi.org/10.1007/s10796-020-10084-7
Goyal, H. R., Ghanshala, K. K., & Sharma, S. (2021). Flash flood risk management modeling in indian cities using IoT based reinforcement learning. Materials Today: Proceedings, 46, 10533–10538. https://doi.org/10.1016/j.matpr.2021.01.072
Greenhalgh, T., & Peacock, R. (2005). Effectiveness and efficiency of search methods in systematic reviews of complex evidence: audit of primary sources. BMJ, 331(7524), 1064–1065. https://doi.org/10.1136/bmj.38636.593461.68
Guerrero, J. C. (2022). Firefighters relying on artificial intelligence to fight California wildfires. In California Dreaming. ABC 7 News. https://abc7news.com/california-wildfires-artificial-intelligence-wifire-lab-burnpro-3d/11596528/
Harasimiuk, D. E., & Braun, T. (2021). Regulating Artificial Intelligence: Binary Ethics and the Law. Routledge. https://doi.org/10.4324/9781003134725
Harirchian, E., Kumari, V., Jadhav, K., Rasulzade, S., Lahmer, T., & Raj Das, R. (2021). A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings. Applied Sciences, 11(16), 7540. https://doi.org/10.3390/app11167540
Hsu, C.-C., & Sandford, B. A. (2007). The Delphi Technique: Making Sense of Consensus. https://doi.org/10.7275/PDZ9-TH90
Huot, F., Hu, R. L., Goyal, N., Sankar, T., Ihme, M., & Chen, Y.-F. (2022). Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire Spreading From Remote-Sensing Data. In IEEE Transactions on Geoscience and Remote Sensing (Vol. 60, p. 13). https://doi.org/10.1109/TGRS.2022.3192974
Hussein, S. (2019). Using remote sensing data for predicting potential areas to flash flood hazards and water resources. Remote Sensing Applications, 12. https://doi.org/10.1016/j.rsase.2019.100254
Iaccarino, A. G., Gueguen, P., Picozzi, M., & Ghimire, S. (2021). Earthquake Early Warning System for Structural Drift Prediction Using Machine Learning and Linear Regressors. Frontiers in Earth Science, 9. https://doi.org/10.3389/feart.2021.666444
Ionescu, B., Ghenescu, M., Rastoceanu, F., Roman, R., & Buric, M. (2020). Artificial Intelligence Fights Crime and Terrorism at a New Level. IEEE MultiMedia, 27(2), 55–61. https://doi.org/10.1109/mmul.2020.2994403
IPCC. (2023). Intergovernmental Panel on Climate Change. Summary for Policymakers. Synthesis Report of the IPCC Sixth Assessment Report (AR6) (P. Arias, M. Bustamante, I. Elgizouli, G. Flato, M. Howden, C. Méndez-Vallejo, J. J. Pereira, R. Pichs-Madruga, S. K. Rose, Y. Saheb, R. Sánchez Rodríguez, D. Ürge-Vorsatz, C. Xiao, N. Yassaa, J. Romero, J. Kim, E. F. Haites, Y. Jung, R. Stavins, … C. Péan, Eds.). Intergovernmental Panel on Climate Change. https://doi.org/10.59327/ipcc/ar6-9789291691647
Ismail-Zadeh, A. T., Cutter, S. L., Takeuchi, K., & Paton, D. (2016). Forging a paradigm shift in disaster science. Natural Hazards, 86(2), 969–988. https://doi.org/10.1007/s11069-016-2726-x
ISO 22395. (2018). Security and resilience—Community resilience—Guidelines for supporting vulnerable persons in an emergency.
ITU. (2019). International Communications Union. GET Background document - Emergency Telecommunications - Disruptive technologies and their use in disaster risk reduction and management. https://www.itu.int/en/ITU-D/Emergency-Telecommunications/Pages/Events/2019/GET-2019/Disruptive-technologies-and-their-use-in-disaster-risk-reduction-and-management.aspx
Izumi, T., Shaw, R., Djalante, R., Ishiwatari, M., & Komino, T. (2019). Disaster risk reduction and innovations. Progress in Disaster Science, 2, 100033. https://doi.org/10.1016/j.pdisas.2019.100033
Jean, C., Hall, T. E., & Vickery, J. (2023). Intersectionality as a Forward-Thinking Approach in Disaster Research. In Oxford Research Encyclopedia of Natural Hazard Science. Oxford University Press. https://doi.org/10.1093/acrefore/9780199389407.013.425
Kaku, K. (2019). Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in Sentinel Asia. International Journal of Disaster Risk Reduction, 33, 417–432. https://doi.org/10.1016/j.ijdrr.2018.09.015
Kankanamge, N., Yigitcanlar, T., & Goonetilleke, A. (2021). Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney, Melbourne and Brisbane. Telematics and Informatics, 65, 101729. https://doi.org/10.1016/j.tele.2021.101729
Katz, J., & Aspden, P. (1997). Motivations for and barriers to Internet usage: Results of a national public opinion. Internet Research: Electronic Networking Applications and Policy, 7, Number 3.
Kaur, H., & Sood, S. K. (2019). Fog-assisted IoT-enabled scalable network infrastructure for wildfire surveillance. Journal of Network and Computer Applications, 144, 171–183. https://doi.org/10.1016/j.jnca.2019.07.005
Kelley, P. G., Yang, Y., Heldreth, C., Moessner, C., Sedley, A., Kramm, A., Newman, D. T., & Woodruff, A. (2021, July). Exciting, Useful, Worrying, Futuristic. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. https://doi.org/10.1145/3461702.3462605
Khan, T. A., Alam, M., Shahid, Z., & Ahmed, S. F. (2018). Artificial Intelligence based Multi-modal sensing for flash flood investigation. Applied Sciences, 6.
Lucivero, F., Swierstra, T., & Boenink, M. (2011). Assessing Expectations: Towards a Toolbox for an Ethics of Emerging Technologies. NanoEthics, 5(2), 129–141. https://doi.org/10.1007/s11569-011-0119-x
Majumdar, S., Kumar Pani, S., Kumar Singh, S., Garg, L., Bilas Pachori, R., & Zhang, X. (2021). The Role of Remote Sensing and GIS in Military Strategy to Prevent Terror Attacks. In Intelligent Data Analytics for Terror Threat Prediction: Architectures, Methodologies, Techniques and Applications (pp. 79–94). Scrivener Publishing.
Marhain, S., Ahmed, A. N., Murti, M. A., Kumar, P., & El-Shafie, A. (2021). Investigating the application of artificial intelligence for earthquake prediction in Terengganu. Natural Hazards, 108(1), 977–999. https://doi.org/10.1007/s11069-021-04716-7
Marshall, T. M. (2020). Risk perception and safety culture: Tools for improving the implementation of disaster risk reduction strategies. International Journal of Disaster Risk Reduction, 47, 101557. https://doi.org/10.1016/j.ijdrr.2020.101557
Meier, M., Kodera, Y., Böse, M., Chung, A., Hoshiba, M., Cochran, E., Minson, S., Hauksson, E., & Heaton, T. (2020). How Often Can Earthquake Early Warning Systems Alert Sites With High‐Intensity Ground Motion? Journal of Geophysical Research: Solid Earth, 125(2). https://doi.org/10.1029/2019jb017718
Mishra, A. K. (2021). Remote sensing of extreme flash floods over two southern states of India during North-East monsoon season of 2020. Natural Hazards, 6. https://doi.org/10.1007/s11069-021-04631-x
Mitra, P., Ray, R., Chatterjee, R., Basu, R., Saha, P., Raha, S., Barman, R., Patra, S., Biswas, S. S., & Saha, S. (2016, October). Flood forecasting using Internet of things and artificial neural networks. 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). https://doi.org/10.1109/iemcon.2016.7746363
Mousavi, S. M., & Beroza, G. C. (2022). Deep-learning seismology. Science, 377(6607). https://doi.org/10.1126/science.abm4470
Mousavi, S. M., & Beroza, G. C. (2023). Machine Learning in Earthquake Seismology. Annual Review of Earth and Planetary Sciences, 51(1), 105–129. https://doi.org/10.1146/annurev-earth-071822-100323
Novellino, A., Jordan, C., Ager, G., Bateson, L., Fleming, C., & Confuorto, P. (2018). Remote Sensing for Natural or Man-Made Disasters and Environmental Changes. In Geological Disaster Monitoring Based on Sensor Networks (pp. 23–31). Springer Singapore. https://doi.org/10.1007/978-981-13-0992-2_3
Ogie, R. I., Rho, J. C., & Clarke, R. J. (2018, December). Artificial Intelligence in Disaster Risk Communication: A Systematic Literature Review. 2018 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM). https://doi.org/10.1109/ict-dm.2018.8636380
Petersen, K., & Büscher, M. (2015). Technology in Disaster Response and Management: Narratives of Ethical, Legal, and Social Issues.
Petersen, L., Havarneanu, G., McCrone, N., & Markarian, G. (2023, May). Proceedings of the International ISCRAM Conference. https://doi.org/10.59297/hvjn6870
Pohl, C. (2020). Delphi poll. Zenodo. https://doi.org/10.5281/ZENODO.3716976
Rathje, E. M., & Franke, K. (2016). Remote sensing for geotechnical earthquake reconnaissance. Soil Dynamics and Earthquake Engineering, 91, 304–316. https://doi.org/10.1016/j.soildyn.2016.09.016
Ray, P. P., Mukherjee, M., & Shu, L. (2017). Internet of Things for Disaster Management: State-of-the-Art and Prospects. IEEE Access, 5, 18818–18835. https://doi.org/10.1109/access.2017.2752174
Rieland, R. (2018). Can Artificial Intelligence Help Stop School Shootings? https://www.smithsonianmag.com/innovation/can-artificial-intelligence-help-stop-school-shootings-180969288/
Sakurai, M., & Shaw, R. (2021). Emerging Technologies for Disaster Resilience: Practical Cases and Theories. In Disaster Risk Reduction. Springer Singapore. https://doi.org/10.1007/978-981-16-0360-0
Schubert, K., & Klein, M. (2020). Das Politiklexikon (7. aktualisiert und erweiter. Aufl). Bundeszentrale für politische Bildung.
Seydoux, L., Balestriero, R., Poli, P., Hoop, M. de, Campillo, M., & Baraniuk, R. (2020). Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17841-x
Shaw, R. (2020). Thirty Years of Science, Technology, and Academia in Disaster Risk Reduction and Emerging Responsibilities. International Journal of Disaster Risk Science, 11(4), 414–425. https://doi.org/10.1007/s13753-020-00264-z
Singer, N. (2022). Schools Are Spending Billions on High-Tech Defense for Mass Shootings. https://www.nytimes.com/2022/06/26/business/school-safety-technology.html
Staniforth, A., & Akhgar, B. (2015). Harnessing the Power of Big Data to Counter International Terrorism. In Application of Big Data for National Security (pp. 23–38). Elsevier. https://doi.org/10.1016/b978-0-12-801967-2.00003-3
Steyaert, J., & Gould, N. (2009). Social Work and the Changing Face of the Digital Divide. British Journal of Social Work, 39(4), 740–753. https://doi.org/10.1093/bjsw/bcp022
Stojadinović, Z., Kovačević, M., Marinković, D., & Stojadinović, B. (2021). Rapid earthquake loss assessment based on machine learning and representative sampling. Earthquake Spectra, 38(1), 152–177. https://doi.org/10.1177/87552930211042393
Sun, W., Bocchini, P., & Davison, B. D. (2020). Applications of artificial intelligence for disaster management. Natural Hazards, 103(3), 2631–2689. https://doi.org/10.1007/s11069-020-04124-3
Surya, L. (2020). Fighting fire with AI: Using deep learning to help predict wildfires in the US (Vol. 8, p. 5).
Taale, A., Ventura, C. E., & Marti, J. (2021). On the feasibility of IoT-based smart meters for earthquake early warning. Earthquake Spectra, 37(3), 2066–2083. https://doi.org/10.1177/8755293020981964
UNDRR. (2022). United Nations Office for Disaster Risk Reduction (Ed). Our world at risk: Transforming governance for a resilient future.
U.N.D.R.R. (2024). Disaster Risk Reduction. https://www.undrr.org/terminology/disaster-risk-reduction
Union, I. T. (2019). ITU GET Background document—Emergency Telecommunications—Disruptive technologies and their use in disaster risk reduction and management [ITU.]. https://www.itu.int/en/ITU-D/Emergency-Telecommunications/Pages/Events/2019/GET-2019/Disruptive-technologies-and-their-use-in-disaster-risk-reduction-and-management.aspx
Verma, S., Kaur, S., Rawat, D. B., Xi, C., Alex, L. T., & Zaman Jhanjhi, N. (2021). Intelligent Framework Using IoT-Based WSNs for Wildfire Detection. IEEE Access, 9, 48185–48196. https://doi.org/10.1109/access.2021.3060549
Vickery, J. (2017). Using an intersectional approach to advance understanding of homeless persons’ vulnerability to disaster. Environmental Sociology, 4(1), 136–147. https://doi.org/10.1080/23251042.2017.1408549
Vogel, C., Zwolinsky, S., Griffiths, C., Hobbs, M., Henderson, E., & Wilkins, E. (2019). A Delphi study to build consensus on the definition and use of big data in obesity research. International Journal of Obesity, 43(12), 2573–2586. https://doi.org/10.1038/s41366-018-0313-9
Wu, A., Lee, J., Khan, I., & Kwon, Y.-W. (2021, December). CrowdQuake+: Data-driven Earthquake Early Warning via IoT and Deep Learning. 2021 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata52589.2021.9671971
Zambrano, A. M., Perez, I., Palau, C., & Esteve, M. (2017). Technologies of Internet of Things applied to an Earthquake Early Warning System. Future Generation Computer Systems, 75, 206–215. https://doi.org/10.1016/j.future.2016.10.009
Zhao, X., Lovreglio, R., Kuligowski, E., & Nilsson, D. (2020). Using Artificial Intelligence for Safe and Effective Wildfire Evacuations. Fire Technology, 57(2), 483–485. https://doi.org/10.1007/s10694-020-00979-x
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Lorena Daphna Kuratle, Irina Dallo , Michèle Marti, Stauffacher Michael
This work is licensed under a Creative Commons Attribution 4.0 International License.
Funding data
-
Horizon 2020 Framework Programme
Grant numbers 101021746
