Jul 2026· The social science· 0 citations· 62 references
Abstract
To manage complex disaster risks, it is important to develop a response that spans multiple areas of expertise. Bridging the gap between law, social sciences, and natural sciences is an important part of any disaster risk reduction. It helps systems adapt quickly as AI technologies are constantly changing and have significantly impacted where law and the natural environment intersect, influencing legal systems and environmental policies, and how legal and environmental issues can prevent AI from having a greater impact on society and the economy. As part of a participatory assessment of production, with the assistance of legal experts, social and environmentalists, the key principles of responsible data analysis are proposed. These principles focus on security, transparency, fairness, accountability, and the ability to challenge or challenge decisions. This conversation describes how different disciplines can work together to create flexible legal systems that use AI, while drawing knowledge from the environmental and social sciences. The way environmentalists and decision-makers talk about useful and accurate information leads to differences that make the use of artificial intelligence difficult due to legal issues related to the reliability, reliability, and contentiousness of disaster management systems. If social media is useful for disaster risk reduction through AI, it's important to consider legal issues related to who is responsible and how sensitive the information used in disaster management is. Ideas for a fair and responsible process focus on the environment and encourage discussions on social and economic issues related to public participation. AI is also very important in education, as it brings together the next generation of law, social and natural sciences to jointly find solutions that bring together different disciplines in a balanced way. AI tools can be very useful in emergency management, but it's important to use them fairly and responsibly. You need to think about things like possible unfair benefits, clear explanations, and protecting people's personal data. Careful use of AI techniques, considering how AI, laws, and environmental risks interact with each other, helps create equitable and sustainable ways to collect and use data.
Irrespective of the type of disaster, disaster management requires a systematic approach to mitigate, respond to, and recover from disaster. Organisations, scientists, and researchers worldwide are shifting to Artificial Intelligence (AI) to minimise the damage caused by natural events. With the growing challenges of climate instability, the influence of AI in disaster management is requisite. Artificial intelligence, with its unprecedented capabilities in predicting and preparing for disasters, along with mitigating and lessening the damage, aids in better and rapid responses to disasters. The study delves into the multi-aspect abilities of AI across the disaster management cycle, indicative of its worth from readiness to the recovery phases. The paper illustrates the groundbreaking potential of AI in disaster management, concentrating not only on its strategic implications but also on its predictive capabilities across different regions of the world. In addition to the above, the article also discusses the use cases of AI in transforming disaster management and the challenges hindering the adoption of this technology.
Anusha Thakur· International Journal of Kno...· 0 citations
Rapid urbanization and escalating climate-related risks have significantly heightened the disaster vulnerability of cities worldwide. Traditional urban planning methods, constrained by their reliance on static historical data and reactive strategies, are increasingly inadequate for addressing contemporary hazard complexities. Artificial Intelligence (AI) presents a transformative opportunity for disaster-resilient urban planning through data-driven insights and dynamic decision-making capabilities. This paper evaluates the role of AI in enhancing urban resilience and proposes a conceptual framework grounded in a systematic review of peer reviewed literature and comparative global case studies. The study employs a qualitative review methodology encompassing an examination of academic publications, policy reports, and case studies from disaster prone metropolitan areas. Secondary data were sourced from Scopus, Web of Science, institutional databases, and publicly accessible urban and climate datasets. Results indicate that AI-enabled approaches demonstrate superior capabilities in risk recognition, integrated data analytics, and the formulation of proactive, adaptive planning strategies compared to conventional methods. Comparative case study analysis of Chennai, Rotterdam, Tokyo, and Singapore reveals that the efficacy of AI applications is contingent upon data availability, technological infrastructure, and institutional governance capacity. The study concludes that while AI holds substantial potential to transform disaster-resilient urban design, its effective implementation necessitates robust institutional frameworks, ethical governance, and context-specific adaptation, particularly in developing regions.
Senthil M· 2026 11th International Conf...· 0 citations
Humans are faced with an ever-increasing amount of risk due to disaster events and extreme weather. Understanding this risk via the use of data and scientific findings is critical for decision makers who are tasked with employing limited resources to strengthen resilience in their communities. Data often resides in silos and is spread across organizations and government agencies. Given the large amount of information that is available, but sometimes difficult to understand outside of a research perspective, there are many technologies that can be employed to help translate this information into easier-to-understand interfaces.
A technology system that effectively synthesizes data and translates information (from trusted and specific places) from across multiple sources and helps decision makers “connect the dots” is crucial for future decision-making tools. As climate change exacerbates disasters, these technologies have the potential to greatly improve the outcomes of the inevitable events that impact peoples' lives and livelihoods.
The primary difficulty is not acquiring data, but synthesizing, interpreting, and communicating it effectively. Cinematic Data Visualization (CDV) is essential here. Moving beyond static graphics, engaging CDV implementation is transformative, vastly improving our collective ability to understand complex, disparate information. By offering shared, interactive environments, CDV facilitates interdisciplinary communication, allows rapid exploration of uncertainties and scenarios, and establishes a common operational picture necessary for timely risk management decisions across all phases of a hazard event. These advanced visualization methods fundamentally accelerate the transition from
data visualization
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actionable insight
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Shayna S. Solis, A. Silcott, Zoey Armstrong et al.· GeoHorizons· 0 citations
With the advancement of technology and growing climate crisis, artificial intelligence has emerged as a significant tool for Epredicting change in the climate and natural calamities with precision. AI models, today process and analyse large data sets to provide minute details regarding a slight rise in the sea level, extreme changes in weather and increased carbon emissions that traditional physics-based models fail to recognize. However, the integration of artificial intelligence into climate prediction introduces legal challenges that remain largely unaddressed by international as well as domestic frameworks. The core issue addressed in this paper is the responsibility gap created by the ‘black-box’ nature of AI- based climate predictions. When policy-oriented decisions such as urban zoning, investments in infrastructure and emergency evacuations are based on algorithm that later proves to be biased or inaccurate on the basis of data stored in the model, the problem of accountability arises. Furthermore, the paper examines the friction surrounding data governance and the importance of ‘right to information’ for public climate adaptation. The doctrinal analysis of emerging legislations such as the EU AI Act and the India’s Digital Personal Data Protection Act, 2023 will be done in order to evaluate how precautionary principle of environmental law can be implemented within the artificial intelligence framework. This paper proposes Sustainability by Design framework along with other suggestions. This framework advocates for mandatory transparency in training data, standardizing audit protocols for AI based climate model and a multifaceted liability framework to ensure that AI serves as a reliable instrument for climate justice.
Varalika Nigam, Suryanshi Gupta· International journal of com...· 0 citations
Artificial intelligence is profoundly reshaping the paradigm of urban governance. On one hand, AI brings efficiency gains, resource optimization, and enhanced resilience — automated administration, precise services, and intelligent operations make cities run more efficiently. On the other hand, the accompanying risks cannot be ignored: algorithm optimization, responsible data governance, transparent decision-making, and inclusive digital development. These challenges highlight the importance of balancing technological innovation with public values. To overcome this predicament, a shift toward a people-centered governance paradigm is needed, where human-AI collaboration and inclusive governance establish a dynamic balance between efficiency and equity, innovation and regulation.
Min Yuan· Trends in Social Sciences an...· 0 citations
As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology's capabilities, limitations, and attendant risks in context requires analysis of full sociotechnical systems. Sociotechnical analysis of risks in highly complex systems provides clear lessons for the design and evaluation of AI systems, transcending a technical focus on reliable or"responsibly designed"components to understand risks at a systems level. Human-made catastrophes have been studied for decades because of the severity of these events: consider Chernobyl, Three Mile Island, Fukushima-Daiichi, Bhopal, the Challenger disaster. A common misconception is that these kinds of events are freak accidents, resulting from the inherently unforeseeable interactions in complex systems. Closer examination reveals that the risks and hazards were well-known beforehand but not acted upon due to social structural, political and economic factors. We outline several areas where the development and use of AI can benefit from learning these unlearned lessons: improved risk perception, communication, and analysis at the organizational level; traceability of requirements and responsibilities; and holistic approaches to responsibility and safety that include social and organizational dynamics as first-order engineering concerns. For each area, we offer concrete unlearned lessons and exemplify how they led to failure in prior accidents as well as examples of how these lessons remain unlearned for modern computing systems, particularly AI.
Joshua A. Kroll, A. Smart, R. Geiger et al.· Harvard data science review· 0 citations