Jul 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 568-577· 0 citations
Abstract
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.
The rapid development and widespread application of artificial intelligence (AI) have sparked intense discussions on how to deploy responsible AI systems in a manner aligned with human values and ethical standards. Compared to fields like healthcare, energy, or finance, the application of AI in groundwater is relatively limited, and research on responsible AI is even more scarce. Taking the middle reaches of the Heihe River Basin as the study area, this paper proposes six Responsible AI principles: transparency, technical robustness, privacy governance, fairness, accountability, and sustainability. LSTM and Transformer time-series models are developed using multi-source hydrometeorological data, and validated via post-hoc interpretability, Monte Carlo simulation, and scenario analysis. The results show that Transformer outperforms LSTM in accuracy, robustness, and interpretability, demonstrating the operability and practical value of Responsible AI principles in groundwater prediction to support sustainable water management under climate change and human activities.
Chong-De Chen, Yulu Zhang, Qingxi Guo et al.· 0 citations
Climate change is increasingly reshaping the sustainable development of the ocean through ocean warming, deoxygenation, acidification, and other compounding stressors. Against this backdrop, environmental impact assessment (EIA) has become a pivotal governance instrument for anticipating and reducing the climate-related impacts of human activities at sea. From the United Nations Convention on the Law of the Sea (UNCLOS) to the Agreement on Biodiversity Beyond National Jurisdiction (the BBNJ Agreement), regulatory expectations for marine EIAs are moving toward more structured thresholds, procedural workflows, and reporting obligations. At the same time, rapid advances in climate artificial intelligence (climate AI), such as machine-learning forecasting, deep-learning nowcasting, and agentic AI workflows, are expanding the ability to produce timely, high-resolution, and probabilistic climate information from heterogeneous climate data streams. These capabilities can strengthen climate-related EIAs by combining short-term forecasting and nowcasting for early warning, long-term observation and monitoring for dynamic baselines, and scenario-based climate modelling for impact estimation and decision support. Climate AI can therefore be integrated throughout the EIA workflow rather than appended as an auxiliary layer, translating climate data into regulatory evidence under changing marine-climate conditions. To ensure regulatory robustness and accountability, implementation should be grounded in evidence standards and quality assurance, transparent and auditable documentation, human oversight, responsibility allocation, and formal mechanisms for cross-institutional data sharing. We argue that a standards-driven, AI-enabled EIA framework can improve the relevance, reviewability, and robustness of marine EIA decisions, supporting long-term ocean sustainability under accelerating climate risks.
Xuan Gong, Le Cheng· Frontiers in Marine Science· 0 citations
Artificial intelligence (AI) is increasingly considered a cornerstone of contemporary discourse concerning geopolitics and geoeconomics.The domain appears to be largely constrained by a teleological, zero-sum narrative that prioritizes the question of which state will secure AI supremacy in this great power competition.This perspective diverts attention from a more critical and empirically accessible phenomenon that is the increasing utilization of artificial intelligence, data analytics and machine learning as fundamental instruments for geopolitical risk mitigation, supply chain oversight and state-led regulatory enforcement within an increasingly fragmented and competitive global economic landscape. This paper reconceptualises AI-driven supply chain risk management systems not merely as operational tools, but as critical sites of algorithmic governance where technical properties such as bias detection mechanisms, data security protocols, and cross-jurisdictional information routing function as direct variables of state power. By integrating structural realism with power transition theory, this study moves beyond aggregate AI capability metrics to examine the systemic institutionalisation of these technologies. The study employs a multidimensional evaluation framework that positions algorithmic bias exposure, data security, structural dependency concentration, and regulatory fragmentation risk as fundamental determinants of geopolitical leverage,implicating how automated systems actively reshape the global distribution of risk, information, and strategic control.To conceptualize the structural friction inherent in global digital supply chains, this paper introduces the 'algorithmic sovereignty trilemma.' This construct demonstrates why technical optimisation, data sovereignty, and cross-bloc interoperability cannot be simultaneously maximised, effectively mapping the fundamental trade-offs states must navigate amidst technological decoupling. The framework is applied illustratively to the semiconductor and critical-minerals supply chains implicated in the ongoing US-China technological rivalry, and is situated against the European Union’s AI Act, United States export-control practice, and China’s data-security architecture. The paper argues that algorithmic supply chain governance is best understood not as a neutral optimisation problem but as a site where distributional power, sovereignty claims, and systemic risk are jointly produced.The paper concludes that by foregrounding the technical architecture of supply chain oversight, states can better anticipate how algorithmic dependencies amplify security dilemmas and harden geopolitical fissures .It suggests a recalibration of national policy toward the creation of resilient, audit-transparent oversight architectures that mitigate asymmetric vulnerabilities and prevent the weaponization of critical data-flow interdependencies. By integrating technological expertise with systemic structural analysis, the research underscores how the securitization of AI value chains inevitably promotes the emergence of techno-blocs, which utilize exclusive infrastructure partnerships to consolidate authority over global digital inputs.
Iqra Nissar, S. Pathak, Yogesh Kumar Gupta· International journal of com...· 0 citations
Generative artificial intelligence (AI) presents significant governance challenges due to its rapid technological advancement, emergent behavior, and the inability of existing regulatory institutions to keep pace with its development. This paper presents a qualitative, scenario-based forecast of the global regulation of generative AI between 2025 and 2035. Drawing on scenario planning, horizon scanning, and evidence from AI safety research, digital governance theory, and policy analytics, the study identifies two critical uncertainties shaping future regulatory outcomes: the level of international regulatory coordination and the pace of AI safety and interpretability advances. These uncertainties form the basis for two contrasting scenarios: Regulated Convergence, characterized by strengthened international cooperation, consolidated oversight institutions, and augmented governance supported by technical verification mechanisms; and Fragmented Acceleration, in which geopolitical competition, institutional fragmentation, and limited progress in AI safety hinder effective governance. The analysis evaluates the policy trade-offs associated with each scenario, including regulatory capacity, licensing regimes, transparency requirements, international coordination, and open-source AI development. The findings suggest that effective governance by 2035 will depend on sustained institutional investment, adaptive regulatory frameworks, technical advances in AI safety, and greater international collaboration. Without these conditions, fragmented governance is likely to remain the dominant trajectory, increasing regulatory gaps and societal risks associated with frontier AI systems.
Jimmy Kinyonyi Bagonza, Mathias Ndungu· World Journal of Advanced Re...· 0 citations
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.
R. Paper, Research Supervisor Prof, Dominique Ferraro· The social science· 0 citations
Climate extremes, conflict, and population displacement converge in the Horn of Africa to accelerate outbreaks of climate-sensitive infectious diseases, whereas existing health surveillance systems remain fragmented and largely reactive. This Perspective examines the potential of artificial intelligence (AI) to strengthen climate–health early warning by integrating satellite earth observations, routine disease surveillance, and mobility-based vulnerability indicators into anticipatory decision support systems. Drawing on global experience and region-specific constraints, we identified critical barriers to implementation, including data fragmentation, infrastructure gaps, workforce shortages, governance silos, and unresolved ethical risks. We propose a five-layer conceptual framework for an AI-enabled Climate–Health Early Warning System (CHEWS) tailored to fragile and conflict-affected settings, alongside a phased regional policy roadmap anchored within the Intergovernmental Authority on Development (IGAD). Emphasizing data sovereignty, participatory governance, and privacy-by-design, this study positions AI-CHEWS as a feasible pathway for shifting the region from reactive outbreak responses to anticipatory public health actions that enhance climate resilience and equity. Clinical Trial Number: The authors declare that they have no competing interests.
Ahmed Abdiaziz Alasow, Y. H. Abdi, Abdifatah Ahmed Hersi et al.· Globalization and Health· 0 citations