Confronting risks at the intersection of climate change and artificial intelligence: The promise and perils of rights-based approaches

Amidst heightened hype in the capacity of artificial intelligence (AI) technologies to address climate change, this article begins by elaborating some of the risks and concerns that arise from AI technologies’ extractive climate consumptionism, deployment within climate mitigation and adaptation projects, entanglement in processes of climate securitisation, and contribution to climate discourse. The article then turns to explore the extent to which human rights law (HRL) may be harnessed to address such risks and concerns across three registers of human rights struggles – argumentative, aesthetic, and affective. The article emphasises that if HRL is to be oriented towards addressing the risks and concerns that arise at the intersection of climate change and AI, it is crucial that the terms of human rights mobilisations are driven and informed by the communities most and disproportionately impacted by climate applications of AI across the entire AI lifecycle.

Masters or Slaves? AI and the Future of Humanity

PurposeIn the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.Design/methodology/approachTo explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.FindingsThe investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.Originality/valueThis study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

This study investigates how repo rates and the nominal effective exchange rates affect stock market capitalization between 2016M1 and 2022M1, with a focus on the threshold level of investment in artificial intelligence. To analyze the data, econometrics techniques were employed. The Augmented Dickey-Fuller test confirmed that while market capitalization and repo rates became stationary after the first difference that of investments in artificial intelligence and the nominal effective exchange rates were stationary at level. A threshold model identified the threshold level of investment in artificial intelligence. The findings indicate that repo rates and the nominal effective exchange rates positively influence stock market capitalization when the threshold level of investment in artificial intelligence is below 7.7647. However, above the threshold, repo rates and the nominal effective exchange rates negatively affect stock market capitalization. This study concludes that the negative impact of the repo rates and the nominal effective exchange rates on stock market capitalization at higher levels of investment in artificial intelligence should be internalized through government subsidies that reduce the production cost of firms. However, the SARB should consistently manage the exchange rates from growing out of proportion.

Technology based on artificial intelligence (AI) is a revolutionary force that is changing economies, civilizations, and industries all over the world. AI, which has its roots in computer science and cognitive psychology, is a wide range of tools and methods designed to make robots capable of doing activities that have historically required human intellect. This abstract examines the many facets of artificial intelligence (AI) technology, including its fundamentals, uses, difficulties, and ramifications. Artificial Intelligence (AI) technology comprises several subfields such as robotics, computer vision, natural language processing, machine learning, and expert systems. Particularly, machine learning techniques have propelled incredible progress by allowing computers to learn from data and make judgments or predictions without the need for explicit programming. Natural language processing allows machines to comprehend, interpret, and produce human language, hence facilitating human-computer interaction. Machines can now see, analyze, and interpret visual data from the real world thanks to computer vision technology. Applications of AI technology may be found in a wide range of industries, including manufacturing, healthcare, finance, transportation, agriculture, education, and entertainment. AI-powered solutions help in drug discovery, medical imaging analysis, diagnosis, and customized therapy in the healthcare industry. AI algorithms are used in finance to power automated trading, fraud detection, risk assessment, and customer support. AI makes it possible for transportation to include predictive maintenance, traffic management, and driverless cars. Artificial Intelligence enhances supply chain management, quality assurance, and production processes in manufacturing. AI technology has the potential to revolutionize many industries, but it also comes with dangers and problems. These include privacy concerns, security hazards, ethical dilemmas, issues with prejudice and fairness, and effects on society and employment. Responsible AI methods, legal frameworks, multidisciplinary cooperation, and ethical standards are all necessary to meet these issues. Future prospects for AI technology development include the ability to solve challenging issues, spur creativity, increase productivity, and improve quality of life. But to fully utilize AI, one must take a comprehensive strategy that strikes a balance between the advancement of technology and ethical issues, human values, and social well-being. In summary, artificial intelligence (AI) technology is at the vanguard of innovation, presenting never-before-seen possibilities to transform whole sectors, spur economic expansion, and tackle global issues. AI has the ability to usher in a future of greater human-machine collaboration, innovation, and wealth through the promotion of collaboration, transparency, and ethical stewardship. the Ranking of the Artificial Intelligence using the TOPSIS Method . Interpretable Models is got the first rank whereas is the Ethical AI is having the Lowest rank. Keywords: Explainable AI (XAI), Interpretable Models, Ethical AI ,Responsible AI, Robustness and Adversarial Defense, Continual Learning, Federated Learning, Human-Centric AI, AI Governance and Policy

The rapid advance of artificial intelligence (AI) raises profound governance questions for all societies. However, the discourse on AI governance thus far has been led by a small number of countries that are the leaders in AI technology. Little research has been done on how Global South countries can enhance their AI governance capacity. Are the countries of the Global South simply “passengers in flight” – passive adopters of AI technology, norms, and governance practices developed elsewhere? How much priority should Global South countries give to AI governance, and the development of AI governance capacity, in light of the numerous other pressing challenges they face? This article argues that the Global South “passengers in flight” indeed do have the ability to take meaningful steps to improve their AI governance capacity. The first step is to have a conceptual framework for AI governance capacity, which the article recommends would consist of three elements: (i) Steering AI in the Global South will be a long-term process; (ii) Distributional outcomes of AI in the Global South will be socially-determined; and (iii) Enhancing AI governance capacity in Global South countries requires systematic action and learning at the national level. Thus the pressing challenge of governing AI is not primarily a search for today’s most comprehensive regulation, but rather a long-term process of taking practical, proactive local steps to generate knowledge, enact targeted local policies and gain experience and capacity over time. To apply this framework to the diverse conditions in countries of the Global South, the article recommends a process of assessment, policy action and evaluation, as well as several specific areas of policy consideration for national policymakers, academics, enterprises, media and civil society in the Global South. Through a process of systematic development of national AI governance capacity, the passengers in flight can gain a higher degree of agency, and thus better shape their AI journey.

The purpose of this integrative review is to examine recent literature on the intersection of SARS‐CoV‐2 (COVID‐19 novel coronavirus) and climate change that will lead to a greater understanding of the complexities of the urgent pandemic linked with the emerging climate crisis. A literature search for peer‐reviewed, English language, literature published since the pandemic emerged was conducted using Cumulated Index to Nursing and Allied Health Literature (CINAHL), PubMed, and the Cochrane Library. The final sample yielded a total of 22 commentaries, editorials, discussion papers, and a research study that explicitly addressed the intersection of COVID‐19 and climate change. Sixty articles emerged in the initial review of the intersection of the COVID‐19 pandemic and climate change with the final yield of 22 articles deemed valid for inclusion after full text review. With the emergence of COVID‐19 and scholarly discourse that addresses the intersection of the pandemic with climate change, key issues emerged that intersect with policy /advocacy, social justice, and nursing's public health role in clinical practice, education, policy/advocacy, and research/scholarship. Five themes that emerged included the role of public health in COVID‐19 and climate change efforts; global approach addressing human‐environment issues; intersection of COVID‐19 and climate change from a community and global perspective; impacts of COVID‐19, climate change and the environment and professional associations and specialty organizations’ views and responsibilities with a lens on COVID‐19 and climate change. Despite the importance of addressing racial inequities as well as systemic and structural racism that impacts those most affected by climate change and pandemics such as COVID‐19, no literature addressed this topic. Public health nursing has a critical role in addressing climate change and the pandemic response to COVID 19 in the 21st century.

We present a response to the 2018 Request for Information (RFI) from the NITRD, NCO, NSF regarding the "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan." Through this document, we provide a response to the question of whether and how the National Artificial Intelligence Research and Development Strategic Plan (NAIRDSP) should be updated from the perspective of Fermilab, America's premier national laboratory for High Energy Physics (HEP). We believe the NAIRDSP should be extended in light of the rapid pace of development and innovation in the field of Artificial Intelligence (AI) since 2016, and present our recommendations below. AI has profoundly impacted many areas of human life, promising to dramatically reshape society --- e.g., economy, education, science --- in the coming years. We are still early in this process. It is critical to invest now in this technology to ensure it is safe and deployed ethically. Science and society both have a strong need for accuracy, efficiency, transparency, and accountability in algorithms, making investments in scientific AI particularly valuable. Thus far the US has been a leader in AI technologies, and we believe as a national Laboratory it is crucial to help maintain and extend this leadership. Moreover, investments in AI will be important for maintaining US leadership in the physical sciences.

Abstract: As Artificial Intelligence (AI) advances, so do the risks associated with deepfakes, misinformation, and algorithmic bias, posing significant threats to security, privacy, democracy, and societal trust. "Securing AI: Combating Deepfakes, Misinformation, and Bias with Trustworthy Systems" provides a comprehensive analysis of AI security vulnerabilities, adversarial machine learning, AI-driven misinformation, and bias in automated decision-making. The book explores how AI-generated synthetic media, data poisoning attacks, and biased algorithms are being weaponized for cyber fraud, political manipulation, and unethical automation. It delves into defensive strategies, AI forensic tools, cryptographic AI verification, and fairness-aware machine learning techniques to combat these emerging threats. Additionally, the book examines global AI regulations, governance frameworks, and ethical deployment standards that ensure transparency, accountability, and security in AI-driven ecosystems. Through real-world case studies, technical insights, and policy recommendations, this book serves as an essential resource for AI researchers, cybersecurity professionals, policymakers, and technology leaders aiming to develop trustworthy AI systems that resist adversarial manipulation, misinformation campaigns, and algorithmic bias while fostering fair, transparent, and secure AI adoption. Keywords: AI security, adversarial machine learning, deepfake detection, AI-generated misinformation, synthetic media, bias mitigation, AI ethics, AI governance, trustworthy AI, explainable AI (XAI), fairness-aware machine learning, cryptographic AI, federated learning security, digital forensics, algorithmic bias, data poisoning attacks, model robustness, cybersecurity in AI, misinformation detection, deep learning security, AI regulatory policies, zero-trust AI, blockchain-based content verification, ethical AI deployment, secure AI frameworks, AI transparency, AI-driven cyber threats, fake news detection, AI fraud prevention.

Purpose The purpose of this paper is to introduce the artificial intelligence (AI) Citizenship Framework, a model that equips teachers and school library professionals with the tools to develop AI literacy and citizenship in students. As AI becomes increasingly prevalent, it is essential to prepare students for an AI-driven future. The framework aims to foster foundational knowledge of AI, critical thinking and ethical decision-making, empowering students to engage responsibly with AI technologies. By providing a structured approach to AI literacy, the framework helps educators integrate AI concepts into their lessons, ensuring students develop the skills needed to navigate and contribute to an AI-driven society. Design/methodology/approach This paper presents a theoretical framework, developed from the author’s experience as an information and digital literacy coach and teacher librarian across Asia, the Middle East and Europe. The AI Citizenship Framework was created without following specific empirical methodologies, drawing instead on practical insights and educational needs observed in diverse contexts. It outlines a scope and sequence for integrating AI literacy into school curricula. The framework’s components build on existing pedagogical practices while emphasising critical, ethical and responsible AI engagement. By providing a structure for AI education, it serves as a practical resource for school librarians and educators. Findings While no empirical data was collected for this theoretical paper, the AI Citizenship Framework offers a structured approach for school librarians and educators to introduce and develop AI literacy. It has the potential to influence AI education by fostering critical and ethical awareness among students, empowering them to participate responsibly in an AI-driven world. The framework’s practical application can be expanded beyond school librarians to include classroom teachers, offering a comprehensive model adaptable to various educational settings. Its real-world implementation could enhance students’ readiness to engage with AI technologies, providing long-term benefits for both educational institutions and the broader society. Research limitations/implications One limitation of the AI Citizenship Framework is that it has not yet been empirically validated. Future research could focus on testing its practical effectiveness in real-world settings, offering insights that may inform refinements and adaptations to better support school librarians and educators in fostering AI literacy and AI citizenship. Practical implications The practical implication of the AI Citizenship Framework is its application in educational settings to equip students with AI literacy and responsible citizenship skills. School library professionals and teachers can use the framework to integrate AI concepts into curricula, fostering critical thinking, ethical understanding and informed decision-making about AI technologies. The framework provides ready-to-use curriculum plans, enabling educators to prepare students for an AI-driven world. Its adaptability also allows classroom teachers to lead AI literacy initiatives, making it a versatile tool for embedding AI education across subjects and promoting responsible use and engagement with AI technologies in real-world contexts. Originality/value The originality and value of the AI Citizenship Framework lie in its approach to integrate AI literacy into educational contexts, specifically tailored for teacher librarians and school librarians. To the best of the authors’ knowledge, it is the first framework that comprehensively addresses the need for AI literacy from an ethical, critical and societal perspective, while also promoting active participation and leadership in AI governance. The framework equips educators with practical tools and curriculum plans, fostering responsible AI use and engagement. Its adaptable structure ensures it can be implemented by classroom teachers as well, adding significant value to AI education across disciplines and age groups.

Recent technological advances that have already impacted corporate operations include the internet of things, big data analytics, and artificial intelligence, to name just a few. Artificial intelligence (AI) has the most potential to cause a revolution in marketing strategy compared to other forthcoming technologies. In today's corporate sector, artificial intelligence may be beneficial in a variety of contexts. The intellectual and expert consensus is that artificial intelligence will decide the destiny of human civilization. The expansion of information and communication capabilities has transformed the whole world into a massive network of linked nodes. As a result of technology applications, investments in Artificial Intelligence (AI) for huge data insights to deliver business intelligence have surged. Contrary to common opinion, other industries, including healthcare, e-commerce, education, government, and business, also make major use of Artificial Intelligence technology. An increasing number of businesses frequently use AI technology. Professionals across the globe are attempting to determine which artificial intelligence (AI) solutions are most suited for their advertising campaigns. Yet, a detailed review of the study's findings might emphasize the significance of AI and big data in marketing and suggest future research areas in this sector.

Today, the race between the top countries in the field of artificial intelligence continues in the world. The leaders, of course, are the USA and China. Everyone else is catching up. Among those who are catching up, including the European Union. The EU recently presented a strategy for the development of artificial intelligence (AI). The EU White Paper on Artificial Intelligence is a European approach to excellence and trust. The purpose of the article is to determine the main points of the strategy: artificial intelligence is one of the main factors in achieving the goals of sustainable development; creation of a European space, a single data market, to unlock unused data and allow its free movement within the EU and across sectors; creation of European data pools, allowing to create trust within the EU. The subject of research is modern trends in the development of artificial intelligence, changes in financial and economic systems. The research hypothesis is the influence of artificial intelligence on the trends of socio-economic development at different levels. Methods used in the research: comparison, comparison, systematic and analytical approaches, generalization and classification, dialectical and formal logic. Presenting main material. The EU should significantly increase its investments in AI — focusing on public-private partnerships will allow to mobilize private and public investments; launch the European Investment Fund in the amount of 100 million euros in 2020 to finance startups; unification of various competence and skills development centers to increase competitiveness; adopt a program for the development of artificial intelligence to carry out public procurement of AI systems. The originality and practical significance of the obtained results is a call for the creation of a strong European regulatory framework for the development of AI-based products and services with a high degree of trust; special regime for AI with a high level of risk if its technologies are applied in the field of health care, transport, energy, the public sector and for the use of AI that creates legal or significant consequences for the rights of individuals or legal entities, as well as technologies that can have a risk of injury, death or significant material/non-material damage. Conclusions. Society must actively and adequately respond to modern world challenges such as "Industry 4.0", "Society 5.0", sustainable and safe development ("Goals of the SR" - 17 solutions proposed by the UN), promptly respond to military and terrorist threats, cyber security, systemic crises and so on.

The article examines the evolving intersection of privacy rights and artificial intelligence (AI) governance, focusing on the role of international soft law frameworks in shaping privacy protections. Recognizing privacy as a fundamental human right enshrined in various global and regional legal instruments, the study highlights its critical dimensions in the context of emerging AI technologies, which pose unique challenges and opportunities for data governance. The methodology includes an analytical review of significant legislative and policy frameworks, such as the European Union Artificial Intelligence Act, UN General Assembly Resolutions on AI and cybercrime, the UN Global Digital Compact, and the Council of Europe Draft Framework Convention on Artificial Intelligence. These frameworks are assessed for their principles and mechanisms aimed at embedding privacy protections throughout AI systems, emphasizing transparency, accountability, fairness, and international collaboration. Findings indicate growing integration of privacy considerations in AI governance through measures like privacy-by-design, risk management, and restrictions on mass surveillance and untargeted data scraping. Key provisions include robust data governance, transparency requirements, safeguards against discriminatory outcomes, and harmonized privacy standards via international cooperation. The study concludes that international soft law frameworks provide a crucial foundation for embedding privacy protections into AI systems, reflecting a global consensus on safeguarding this right amid technological advances. By harmonizing principles across jurisdictions, fostering multi-stakeholder engagement, and promoting ethical AI development, these initiatives support a human-centric approach to AI governance. The research offers insights for international policymakers to align AI innovation with fundamental rights.

Africa as a continent recognizes the need for Artificial Intelligence (AI) governance frameworks. However, despite initiatives like the African Union's Continental AI Strategy, African nations grappled with challenges in formulating comprehensive AI policies, while established global frameworks provided advanced benchmarks for comparison. To address this disparity, this study conducted a comparative analysis of AI governance in Africa relative to global standards and practices. Employing a comprehensive document analysis methodology, the research examined key policy documents, strategic frameworks, and regulatory guidelines across African, European, American, and Asian contexts. The findings revealed that while the African Union demonstrated commitment to coordinated AI governance, African approaches generally lagged behind global benchmarks in comprehensiveness, formalization, and ethical integration. The study identified a notable fragmentation in AI governance across African nations, contrasting with more unified approaches in other regions. African frameworks emphasized leveraging AI for socio-economic development, diverging from the risk mitigation focus seen in EU regulations. The integration of indigenous African ethical perspectives in AI governance frameworks was limited, presenting both challenges and opportunities. Significant disparities in digital infrastructure and AI capacity between Africa and other regions were found to affect governance implementation. The study concluded that despite these challenges, there was potential for Africa to develop innovative, context-specific AI governance models that could contribute valuable insights to the global discourse on responsible AI development. Recommendations included accelerating the implementation of the Continental AI Strategy, prioritizing investment in digital infrastructure, developing Africa-centric AI ethics frameworks, establishing mechanisms for regular benchmarking against global standards, fostering increased collaboration, and implementing AI literacy programs across the continent.

The need for sustainable solutions has increased globally as a result of the growing environmental problems brought about by urbanization and industrialization. Given this, private investment in artificial intelligence (AI) has become a viable means of promoting environmental sustainability, mainly because of AI's capacity to minimize ecological footprints and maximize resource utilization. This research investigates the role of private investment in AI in promoting environmental sustainability in the United States from 1990 to 2019. It also analyzes the impact of financial globalization, technological innovation, and urbanization by testing the Load Capacity Curve (LCC) hypothesis. The research utilizes stationarity tests, which indicate that the variables are free from unit root problems and exhibit mixed orders of integration. Using the Autoregressive Distributive Lag (ARDL) Model bound test, the analysis finds that the variables are cointegrated in the long run. The short-run and long-run estimations of the ARDL model confirm the existence of the LCC hypothesis in the United States, revealing a U-shaped association between income and load capacity factor. The findings show that private investment in AI has a significant positive correlation with the load capacity factor, thus promoting environmental sustainability. Conversely, technological innovation and financial globalization exhibit a negative correlation with the load capacity factor in both the short and long run. To validate the ARDL estimation approach, the study employs Fully Modified OLS, Dynamic OLS, and Canonical Correlation Regression estimation methods, all of which support the ARDL outcomes. Additionally, the Granger Causality test reveals a unidirectional causal connection from private investment in AI, financial globalization, economic growth, technological innovation, and urbanization to the load capacity factor.