Ethical, Technical, and Data Governance Challenges

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Climate change is increasingly disrupting businesses and ecosystems, creating urgent demand for data-driven adaptation strategies. This study examines how artificial intelligence (AI) can strengthen climate resilience across diverse industries, with particular attention to the innovative business strategies that help organizations respond to global environmental challenges. The objective is to address gaps in existing AI frameworks, focusing on developing countries with resource and technical limitations. The study highlights the significance of AI in fostering sustainable practices, particularly in climate change mitigation. A systematic review of 42 high-quality studies, published between 2010 and 2025 in the Scopus database, was carried out using the PRISMA framework. The analysis identifies key AI applications, technologies, and challenges. Data were organized according to industry applications, technological contributions, and obstacles. Key findings indicate that AI enhances climate risk assessment through predictive modelling, supports adaptive decision-making via scenario analysis, and optimizes resource allocation for sustainability. Applications in renewable energy, precision agriculture, and disaster management are also noted. However, significant barriers persist, including ethical concerns such as algorithmic bias and data privacy, technical complexities, and high financial costs. The review underscores the necessity of collaborative approaches, such as public-private partnerships, and the importance of conducive policy frameworks. The research's originality lies in its comprehensive synthesis of AI applications for climate resilience, offering actionable insights for scholars, practitioners, and policymakers. The findings highlight AI's potential to drive sustainable business models while calling for interdisciplinary research to address scalability and ethical implications in resource-constrained environments.

BackgroundResearchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.MethodsWe conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.ResultsOur findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.ConclusionsThese findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

Individuals are often involved in multiple online social networks. Considering that owners of these networks are unwilling to share their networks, some global algorithms combine information from multiple networks to detect all communities in multiple networks without sharing their edges. When data owners are only interested in the community containing a given node, it is unnecessary and computationally expensive for multiple networks to interact with each other to mine all communities. Moreover, data owners who are specifically looking for a community typically prefer to provide less data than the global algorithms require. Therefore, we propose the Local Collaborative Community Detection problem (LCCD). It exploits information from multiple networks to jointly detect the local community containing a given node without directly sharing edges between networks. To address the LCCD problem, we present a method developed from M method, called colM, to detect the local community in multiple networks. This method adopts secure multiparty computation protocols to protect each network’s private information. Our experiments were conducted on real-world and synthetic datasets. Experimental results show that colM method could effectively identify community structures and outperform comparison algorithms.

Numerous obstacles hinder societal transformation toward a climate-resilient future, often rooted in underlying assumptions prevalent across various domains, including civil society, the public/private sector, and politics. AGORA is a HORIZON Europe project (Grant agreement ID: 101093921) whose aim is to support communities and regions to overcome these obstacles in climate change adaptation. It started in January 2023 and will have a total duration of 3 years. This initiative supports the EU Mission on Adaptation to Climate Change through four main pillars: Conduction four Pilots in different countries (i.e., Spain – Zaragoza; Italy – Rome; Sweden – Malmö; and Germany –Dresden), focusing on workshops and implementing co-creation strategies; Developing improved strategies by understanding stakeholders' needs and climate change risks; over 50 cross-disciplinary stakeholders, including followers from non-Pilot countries like Portugal, are interested in applying the lessons learned; Empowering local communities through societal transformation; it assesses learning tool needs, hosts workshops on pressing issues, and creates digital tools like a Digital Agora, two digital Academies, and an App – a challenging game for simultaneous entertainment and learning; Evaluating climate change policies in different countries and designing adaptation strategies using participatory democratic. In the last year, various activities took place in the four AGORA pilot regions, including inception workshops aimed at bringing together different stakeholders on climate adaptation. These workshops specifically focused on identifying vulnerability, risk drivers, and gaps in local adaptive capacity. The goal was to assess vulnerabilities to expected climate hazards, aligning adaptation priorities with the needs of local communities and fostering community strengthening. Regarding the city of Rome, the inception workshop aligned with the development activities of the city’s Climate Change Adaptation Strategy. The event aimed to foster a fruitful discussion among various local stakeholders regarding adaptation to the anticipated impacts of climate change across multiple sectors (Water - encompassing resource management, drought, and impacts related to precipitation; urban settlements; networks and infrastructure; cultural heritage; health; socioeconomic system; marine and coastal system; agricultural and livestock system; biodiversity and ecosystems). These sectors were identified as key areas most susceptible to risk during the formulation of the Climate Change Adaptation Strategy. The objective was to define the main socioeconomic, structural, and environmental vulnerabilities, as well as the primary needs and critical issues related to the adaptive capacity of the territory and its citizens for each sector under examination. Through a multidisciplinary, integrated approach, AGORA is a growing, dynamic, pan-European community that creates and shares advanced digital tools to enhance awareness. Informed citizens can actively participate and contribute to ensure safe and sustainable development. Hence the project is the meeting point where citizens share knowledge, practices, expertise and needs, interacting with sciences to design and build a more resilient Europe through a living dialogue between local communities.

The Artificial Intelligence (AI) has transformed the world of nutrition by improving the estimation of nutrition habits, maximization of nutritional requirements, quality assessment of food, and creation of population-based health measures. AI applications such as machine learning, natural language processing and computer vision allow the analysis of complex nutritional data to be done precisely and efficiently. In personalised nutrition, AI combines personal eating habits, genome information and health data to provide personalized dietary guidelines which can be used to prevent or control chronic diseases including obesity and diabetes. Nutrient-monitoring devices and image-based dietary monitoring tools provide an even greater support to the continuous and accurate food intake tracking. In the food industry, AI-based algorithms identify quality variations, the predictive quality of the product, and supply chain safety measures. In general terms, AI is involved in terms of the contribution to the nutrition of populations, assessing nutrient shortages, and formulating policy on a large scale by analyzing high levels of dietary data. Although it offers the ability to transform a number of factors, there are still challenges that exist, especially in the areas of data privacy, bias in algorithms, and nutritional information standardization. The combination of AI and the new technologies (the Internet of Things (IoT), block chain, and precision agriculture) can also help to advance sustainable nutrition solutions. This review indicates the latest advances and areas of future advances of AI-based nutrition, with the focus on encouraging health, food security, and personalized diet control in the world. Keywords: Artificial Intelligence (AI), machine learning, personalized nutrition, public health, food quality, precision agriculture, block-chain, sustainable nutrition, data privacy, IoT.

Artificial intelligence (AI) is becoming increasingly prevalent in social work. AI is being used to conduct risk assessments, assist people in crisis, provide supportive services, strengthen prevention efforts, identify systemic biases in the delivery of social services, document clinical encounters, provide clinical supervision, complete administrative tasks, provide social work education, and predict social worker burnout and service outcomes, among other uses. Social workers’ use of AI raises many ethical issues, especially related to informed consent and client autonomy; privacy and confidentiality; transparency; client misdiagnosis; client abandonment; client surveillance; plagiarism, dishonesty, fraud, and misrepresentation; algorithmic bias and unfairness; and use of evidence-based AI tools. It is critically important for social workers who use AI to apply relevant ethical standards and implement protocols designed to protect clients and practitioners.

The construction of modest flats (Rusunawa) in Sadangserang, Bandung City, is planned to use land currently used by the Sadangserang market and station. The temporary relocation of the market and station has the potential to trigger social conflict among traders, public transportation drivers, and the local community. This research is proposed to analyze the potential for social conflict from the perspective of sustainable development planning. The study was conducted using qualitative methods through in-depth interviews with key informants. The research results indicated that the potential for conflict arises from uncertainty about compensation and economic impacts for traders and terminal workers, as well as the loss of cultural identity and access to transportation for the community. Applying sustainable planning principles reduced the potential for conflict through fair compensation schemes, adaptation assistance, participation of affected groups, guaranteed rights to return to their original location, and cross-sectoral communication and coordination. This Research enriches the literature on urban informal infrastructure relocation studies with a perspective of sustainable planning. The research results are in the form of a reference for the government in formulating fair and sustainable relocation policies.

Digital agriculture is rapidly reshaping the interface between agriculture, technology, and the modern nation-state, redefining both food systems and governance structures. This paper examines how Artificial Intelligence (AI), data-driven platforms, and digital infrastructures are transforming agricultural practices, enhancing state capacity, and influencing political power dynamics. Historically, agriculture has played a central role in state formation, from imperial agrarian control and colonial revenue systems to post-independence nation-state governance. In the contemporary digital era, AI-enabled tools such as precision farming, remote sensing, predictive analytics, and algorithmic decision-making have become critical instruments for managing agricultural production, mitigating climate risks, and strengthening food security. The study argues that digital agriculture increases the governing capacity of the nation-state by enabling centralized, data-informed policymaking. AI-driven governance allows states to monitor land, crops, and farmers in real time, anticipate food shortages, optimize resource allocation, and respond more efficiently to environmental variability. By integrating technology with policy, governments can exercise greater control over agricultural systems, reflecting both continuity with historical agrarian governance and the introduction of new mechanisms for oversight and efficiency. At the same time, the paper highlights emerging political and ethical challenges, including data ownership, digital inequality, algorithmic bias, and the concentration of digital resources in the hands of state or corporate actors, revealing tensions between efficiency, equity, and democratic governance. Using a political economy and historical lens, the study situates AI-led agricultural transformation within the broader transition from empire to nation-state, demonstrating both continuity and change in power structures. It emphasizes that digital agriculture is not merely a set of technological tools for productivity but a strategic instrument that reshapes state authority, resource management, and social relations in the twenty-first century.

User-Technological Index of Precision Agriculture (UTIPA) is a comprehensive system based on mutual sharing of opinions and experience within community of people related to precision agriculture - farmers, technology suppliers and researchers. The main benefit of UTIPA is the possibility to use the calculated index level for particular technology (method) for precision agriculture and compare it to other technology with regards to different users, crops, regions etc. It evaluates the principle of a technology but does not take into account concrete products, brands or manufacturers. The index has significance for the presentation of the potential of precision agriculture, development planning and especially for the connection between technological innovativeness and usefulness for practice. The entire solution includes the methodology for the collection, processing and presentation of data and software and is available via a Web interface for all common device platforms. Anyone who has interest in precision agriculture and contributes their knowledge can use the collected data.

Cambodia, a tropical country with high average temperatures and two distinct seasons, a dry and rainy season. Despite the fact that climate change impacts have occurred in the communities, the adaptation mechanism of the local government and community remains very limited. A local level pilot is proposed in Kampong Speu, which is a vulnerable province with a high incidence of poverty. Farmers are expected to face the impact of drought and local communities have limited understanding of related root causes and the potential disasters. The expected loss and damage is still under investigation but anticipated social problems include loss of livelihood, high rates of school dropout, and other aspects of social vulnerability.KeywordsClimate change adaptationClimate impactsVulnerabilityDisaster risksCambodia

The disposition of data from precision agricultural technologies: What do young agriculturalists think?

The climate challenges confronting agriculture are multiple, interconnected and multi-scaled. Agriculture is a source of increasing greenhouse gas emissions, but it is also vulnerable to climate change impacts. Adopting resilient approaches in the agricultural industry can help to contribute to both climate change mitigation and climate change adaptation. Climate-smart agriculture has emerged as a solution to address the multiple challenges of climate change and food security by sustainably increasing productivity, enhancing resilience and reducing greenhouse gas emissions. To date, there is limited scholarly evidence on what constitutes climate-smart agriculture, and how it is framed globally and practiced by smallholder farming communities. This research helps to bridge this gap by analysing the international discourse around climate-smart agriculture, and providing local empirical evidence derived from smallholder farming communities in the Philippines and Timor-Leste. At the broad level, this research aims to identify how climate-smart agriculture within community-based adaptation programs is contributing to the integration of mitigation and adaptation responses to climate change. Drawing from political ecology and climate change (adaptation and mitigation sciences) theories, the research explains how socio-institutional factors – inequality, unequal power relations and social injustice – influence climate-smart agriculture. The theoretical arguments are illustrated with empirical case studies of smallholder farmers and civil society organisations in the two case studies. Using mixed qualitative methods and descriptive analysis of over 150 semi-structured interviews, focus group discussions and participant observation, the research examines climate-smart agriculture practices across three broad categories: vulnerability of smallholder farmers (socioeconomic factors), synergistic relationships (adaptation, mitigation and food security) and governance (socio-institutional determinants). This research argues that mitigation and adaptation interventions are climate-smart for smallholder farmers when they directly address local climate risks, support a combination of adaptation, food security and livelihood strategies, and empower at-risk and marginalised populations. Results indicate that climate-smart agriculture in the Philippines and Timor-Leste are characterised and influenced by multiple socio-institutional factors. The increasing burden of loss and damage as a result of extreme climate events subject women to migration, increased discrimination, loss of customary rights to land, resource poverty and food insecurity. In terms of farming practices implemented by smallholder farmers, most adaptation actions were found to have corresponding positive mitigation, food security and livelihood co-benefits. At the community level, climate-smart interventions are highly location-specific, technically rigorous, involve knowledge-intensive processes, and are influenced by the finance and capacities of local farming communities and implementing partners. Furthermore, of relevance at the global level, this research finds that there is a growing divide between how developed and developing countries frame solutions to the impacts of climate change on agriculture despite agriculture featuring prominently in the United Nations Framework Convention on Climate Change negotiations. Such a divide is limiting the recognition of solutions that integrate mitigation and adaptation opportunities. The insights from the Philippines and Timor-Leste make a compelling case for joint adaptation and mitigation actions in the agriculture sector across three broad policy frontiers. First, implementation of climate-smart agriculture will require participatory platforms that have a focus on livelihood and income opportunities for smallholder farmers. Second, policies and institutions on agriculture, agrarian reform, land use and climate change should mainstream both adaptation and mitigation outcomes using local plans and community level programs. Third, partnerships with community-based organisations and local governments are pivotal to coordinating services with farmers, providing an array of agriculture and climate services generating new knowledge and implementing climate-smart farming solutions.

Smart farming based on Internet of Things (IoT) technologies enables crop farmers to collect real-time data related to irrigation and plant protection processes, aiming to increase production volume, improve product quality, and predict diseases, while optimizing resources and farming processes. IoT devices can collect vast amounts of environmental, soil, and crop performance data, thus building time series data that can be analyzed to forecast and compute recommendations and deliver critical information to farmers in real time. In this sense, the added-value from the farmers’ perspective is that such smart farming techniques have the potential to deliver a more sustainable agricultural production, based on a more precise and resource-efficient approach in the complex and versatile agricultural environment. The aim of this study is to investigate possible advantages of applying the Smart Farming as a Service (SFaaS) paradigm, aiming to support small-scale farmers, by taking over the technological investment burden and offering next generation farming advice through the combined utilization of heterogeneous information sources. The overall results of the pilot application demonstrate a potential reduction of up to 22% on total irrigation needs and important optimization opportunities on pesticides use efficiency. The current work offers opportunities for innovation targeting and climate change adaptation options (new agricultural technologies), and could help farmers to reduce their ecological footprint.

The research utilizes mixed methods to discuss the incorporation of concepts of sustainable livelihoods for climate change adaptation in the eThekwini municipality, South Africa. The municipality identified and implemented climate change adaptation programs incorporating biodiversity, human elements of local economic development and poverty reduction. In poverty reduction co-benefits, the intervention incorporated communityfocused incentives, which mix local biodiversity awareness and efforts to compensate community members who are serving to protect their surrounding natural habitat. Although there is a noted shift in the inclusion of human development, little is reported around the utilization of indigenous knowledge in the project’s sustainability and future continuation within the community. The study recommended that climate change adaptation programs for environmental protection can be holistic if local communities’ cultural practices are incorporated for effective and sustainable interventions.