Government Intelligence Research Articles

Exploring China’s cyber sovereignty concept and artificial intelligence governance model: a machine learning approach

The current global cyber governance model is dominated by Western liberal norms and multi-stakeholder values. Dissatisfied with the status quo, some developing countries like China embrace another governance concept called cyber sovereignty, which advocates more state control. Meanwhile, AI development further enlarges cyberspace’s national security threats, but an international governance framework is absent in the AI realm and China is eager to take the lead in building one. This gives rise to the question: what explains China’s approach to cyber and AI governance? Current studies on cyber sovereignty and China’s AI governance model are mostly qualitative and/or have a small sampling frame, while the meaning of cyber sovereignty is debatable. Therefore, this article applies topic modelling to official/semi-official texts about cyber and AI governance to understand the cyber sovereignty concept and how it shapes China’s approach to AI governance. This article finds that cyber sovereignty is an extension of China’s state-centric view of international order. Not being a passive recipient of norms, China hopes to shape alternative cyber norms to defend national security. Now, since the global community has not reached a consensus over global AI governance, China is exploiting this gap to promote its own set of cyber visions.

Mentor's Musings on Standards, Regulations & Policies Imperatives for Ethics & Governance of Artificial Intelligence

Mentor's Musings on Standards, Regulations & Policies Imperatives for Ethics & Governance of Artificial Intelligence

The Union's Role in Artificial Intelligence Governance and Regulation

The Union's Role in Artificial Intelligence Governance and Regulation

Developing High-Quality Science and Technology Human Resources in Universities through Knowledge Transfer from International Science and Technology Projects: A Theoretical Approach

Universities are crucial in developing high-quality human resources for national development goals. In many countries, universities are regarded as the "cradle of talent," contributing to social progress and enhancing national competitiveness. Alongside their research function, universities serve as "incubators" for high-quality science and technology (S&T) human resources. In addition to training potential personnel such as students, graduate students, and doctoral candidates, universities are also hubs for knowledge transfer activities targeting high-quality S&T human resources. These efforts aim to cultivate a workforce capable of producing innovative and high-quality S&T products, thereby contributing significantly to national development. Accordingly, the implementation of international collaborative projects in the field of S&T has proven to be highly effective in facilitating multi-level knowledge transfer activities. Such initiatives enable universities to achieve streamlined and intelligent governance. Building upon theoretical frameworks and project management approaches, this paper will analyze the critical importance of promoting knowledge transfer activities for developing S&T human resources in universities. Furthermore, it proposes a specialized model for this knowledge transfer process through international S&T projects conducted within universities.

Proximity to Practice: The Role of Technology in the Next Era of Assessment.

The integration of technology into health professions assessment has created multiple possibilities. In this paper, we focus on the challenges and opportunities of integrating technologies that are used during clinical activities or that are completed by raters after a clinical encounter. In focusing on technologies that are more proximal to practice, we identify tradeoffs with different data collection approaches. To maximize the benefits of integrating technology in workplace-based assessment, we describe the importance of using preexisting frameworks from the fields of assessment design, implementation research, and clinical artificial intelligence governance.

How can digitalization be used to develop community resilience in public health emergencies?: A qualitative comparative analysis from China

Community resilience is critical for the government’s response to public health emergencies. With the rapid development of digital technology, leveraging digital tools for grassroots community governance has become increasingly important for the Chinese government. Fuzzy set qualitative comparative analysis (fsQCA) is utilized in this study to establish a framework for investigating the historical development of holistic intelligent governance for community resilience in the context of public health emergencies. Using 31 provincial-level regions in mainland China as research samples and taking the development of community resilience as the outcome variable, the study explores the configurational models and developmental pathways of holistic intelligent governance in enhancing community resilience from a conditional configurational perspective. The results of this study suggest that the mechanisms of community resilience in the face of public health emergencies revolve around five key factors: community self-organization legitimacy, grassroots party-building leadership, policy support, digital governance platform funding, and organizational digitization. However, no single variable alone constitutes a necessary condition for fostering community resilience. Instead, a combination of these factors is required, along with other variables promoting the formation of community resilience, among which community self-organizational legitimacy, grassroots party-building leadership, digital governance platform funding, and organizational digitization serve as the productive basis for promoting the formation of community resilience in holistic intelligent governance at the grassroots level, and are complementary to policy support.

The mediated amplification of societal risk and risk governance of artificial intelligence: technological risk frames on YouTube and their impact before and after ChatGPT

Over the past decade, many experts from academia, business, and civil society have warned of the risks of artificial intelligence (AI), such as large-scale job displacement, data bias against social groups, the use of AI to undermine democracy, the misuse of AI for mass surveillance and/or policing, the unregulated use of autonomous weapons, and others. However, research suggests that news media and social media representations emphasize the benefits over the risks of AI and rarely address issues of risk governance, while social media studies are still rare and largely based on Twitter/X data. This study proposes a theoretical framework of mediated risk communication based on the social amplification of risk framework (SARF), the extended parallel process model (EPPM), and framing. To understand prevalent AI-related risk frames and their role in mediated technological risk amplification/attenuation before and after the release of ChatGPT, a sample of the 501 most viewed YouTube videos about AI published between 2020 and 2023 were analyzed. Four frames with different emphases on societal threat and societal efficacy of AI were found with significant differences between risk/benefit emphases, AI evaluations, risk control/governance measures, and references to risk governance actors. Frames with higher emphasis on societal threat of AI were more likely to be viewed and commented on by YouTube users. AI-related threat frames increased significantly after the release of ChatGPT in 2022, suggesting its role as a risk-amplifying event. Implications for theory, future research, and AI risk governance are discussed.

Proposal for a Strategic Framework on Artificial Intelligence Governance in the Republic of Moldova

The article proposes the development for the Republic of Moldova of a framework for AI governance to align with EU standards, fostering digital transformation and economic growth. The strategic focus includes creating a robust digital ecosystem, enhancing human capacity, and promoting international cooperation. Key eff orts include Moldova’s participation in the EU’s Digital Europe Program and its signing of the Council of Europe’s AI Convention. The Digital Transformation Strategy (2023-2030) aims to develop a digital society and economy, supported by the National Council on Digital Transformation and the Sub-Council for AI and Data Governance (SCIA). Using a multidisciplinary and policy-oriented approach, the article draws on comparative analyses of international frameworks and Moldova’s contextual data to ensure a comprehensive strategy. The results emphasize the potential of responsible AI governance, the implementation of AI regulatory sandboxes, and alignment with EU standards to drive innovation, strengthen regulatory flexibility, and support Moldova’s EU accession aspirations.

Narrative misinformation from a credible source can be discredited with counternarrative

Former government intelligence officer David Grusch became a hot new topic in the UFO world when he declared that the government was hiding an alien ship crash retrieval program. Can this media coverage be influential in increasing belief in UFOs? And can a credible critic of Grusch's claims successfully negate the impact of the media coverage on the acceptance of misinformation? A three-condition experiment (N\,=\,287) showed that a counternarrative can successfully negate the influence of his claims on conspiratorial beliefs. We suggest that these results have practical implications for journalists in their coverage of controversial claims.

Intelligent Data Governance Frameworks : A Technical Overview

This comprehensive article explores the transformative potential of AI-driven data governance frameworks in addressing modern data management challenges. The article examines the limitations of traditional governance methods in the face of exponential data growth, regulatory complexities, and diverse data sources. It delves into the architecture, core components, and benefits of intelligent governance systems that leverage advanced AI technologies such as machine learning, natural language processing, and reinforcement learning. The article highlights significant improvements in operational efficiency, compliance accuracy, and data quality achieved through AI-driven solutions. Furthermore, it investigates future directions, including the integration of explainable AI, federated learning, and blockchain technology, while acknowledging the challenges that need to be addressed for widespread adoption.

The Influence and Strategies of Generative Artificial Intelligence on Academia, Ethics and Society

In the context of the emerging digital environment, generative artificial intelligence (GAI) is quickly becoming implemented in different spheres of life and even begins to replace people in some professions. This means that GAI is leading a new round of technological and industrial revolution. The application and growth of artificial intelligence has improved the society as well as the economy and but has issued many ethical and social problems thus making ethical governance of artificial intelligence more relevant. To contribute to the further development of the concept GAI under the conditions of sustainable development of human society and science, industries and scholars around the world have started researches on this technology. However, as GAI is still an embryonic field, more research efforts must be directed toward this field in the future. Researched articles that compare the use of GAI to the effects on ethical and societal concerns will form the basis of this paper and the resulting discussion of the pros and cons of this technology, this paper considers and demonstrates the difficulties of students, educators, and academics facing the consequences of GAI development. Finally, it has suggestion for the governance of GAI’s next phase of growth.

Artificial intelligence governance framework for healthcare.

Recent advancements in the field of Artificial Intelligence (AI) provide promising applications of this technology with the aim of solving complex healthcare challenges. These include optimizing operational efficiencies, supporting clinical administrative functions, and improving care outcomes. Numerous AI models are validated in research settings but few make their way into useful applications due to challenges associated with implementation and adoption. In this article, we describe some of these challenges, along with the need for a facilitating entity to safely translate AI systems into practical use. The authors propose a new AI governance framework to enable healthcare organizations with a mechanism to implement and adopt AI systems.

Governance of artificial intelligence in Southeast Asia

AbstractGovernance of artificial intelligence (AI) has not achieved global participation. The primary state‐led instrument focusing specifically on the global governance of artificial intelligence is the Global Partnership on Artificial Intelligence (GPAI). Although GPAI aims for broad international participation, the only GPAI member from Southeast Asia is Singapore. GPAI's imbalanced global participation, restrictive membership process, and limited translations are potential barriers to Southeast Asian participation. However, a comparative policy analysis suggests that GPAI members and nonmembers in Southeast Asia have AI governance policies which are largely compatible, despite key differences. This study uses quantitative topic‐modeling and qualitative content analysis to compare the AI governance policies of Indonesia, Malaysia, Thailand, and Vietnam with the policies of Australia and Singapore, as reference GPAI members. The policies of GPAI, Australia, and Singapore emphasise the function of ethics while the policies of Indonesia, Malaysia, Thailand, and Vietnam emphasise the function of human capital development. State‐led, global AI governance efforts could attract more Southeast Asian participation by further emphasising human capital development and deemphasising the function of ethics. GPAI could increase the likelihood of Southeast Asian participation by decreasing its emphasis on political systems, allowing intergovernmental organisations to join, and recruiting all G20 members.

Governing Silicon Valley and Shenzhen: Assessing a New Era of Artificial Intelligence Governance in the United States and China

This article examines recent developments in artificial intelligence (AI) governance in the United States (US) and China, exploring their implications for each country’s development trajectory. Drawing on a framework informed by the philosophy of technology, the article delves into not only the differences between the two countries’ approaches but also the reasons behind these differences. The US, after years of industry self-regulation, is slowly moving towards concrete legislation, while China is centralizing its development and regulatory initiatives. Despite China’s expressed desire for values-pluralistic international governance, existing tensions between the two, coupled with the US’s burgeoning coalition centered around AI with “democratic values,” might pose challenges to collaboration and international governance. Nonetheless, the article contends that both systems can be accommodated within a values-pluralistic human rights framework, potentially paving the way for meaningful international governance efforts.

Comparative Analysis Of Ai Governance In Africa Relative To Global Standards And Practices

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.

Construction and Practice of Education and Teaching Quality Assurance Systems in Applied Colleges and Universities under the Background of Digital Intelligence

This paper discusses the optimization strategy of education and teaching quality assurance systems in applied colleges and universities under the background of digital intelligence. It first summarizes the relevant theories of digital intelligence transformation and analyzes the impact of digital intelligence transformation on higher education. Secondly, this paper puts forward the principles of constructing the quality assurance system of applied colleges, including strengthening the quality assurance consciousness, improving teachers’ digital literacy, and implementing digital intelligence governance. From the practical perspective, this paper expounds on strategies such as optimizing educational teaching resource allocation, constructing a diversified evaluation system of teaching quality, strengthening the construction and training of teaching staff, and innovating teaching management methods. Specific optimization measures are put forward, such as improving policies, regulations, and system guarantees, strengthening cooperation between schools and enterprises, integrating industry, school, and research, building an educational information platform, and improving the monitoring and feedback mechanism of educational quality.

A Comparative Analysis of the EU AI Act and the Colorado AI Act: Regulatory Approaches to Artificial Intelligence Governance

A Comparative Analysis of the EU AI Act and the Colorado AI Act: Regulatory Approaches to Artificial Intelligence Governance

2024 Governance of artificial intelligence and data in Australasian higher education

This whitepaper is a follow-up of the Australasian Council of Open and Digital Education (ACODE) survey in 2023 on the governance of artificial intelligence (AI) and data in Australasian higher education (Selvaratnam & Venaruzzo, 2023). The results then showed the guidelines and policies in this space were still in the early days. Ethical implications were also emerging in tandem with initiatives and the adoption of generative AI in institutions. The latest survey conducted over July and August 2024 is 24 is picking up on the recommendations of the 2023 paper to inform recommendations for practice and further assure quality and equity in higher education. To this end, the JISC AI Maturity Model for Education is used to gauge the sector’s growth in the governance of AI and data both in policy and practice. The outcomes show that the sector is mainly at the experimenting and exploring stage of maturity in engaging with AI. The challenges were mainly in operationalising AI in a comprehensive manner across the enterprise, including increasing AI literacy across staff and students. More institutions are addressing the ethical implications of AI since the last survey; however, it appears that social and emotional wellbeing, and psychological safety still have to be carefully considered.

The Ethical Responsibilities of Researchers in Light of the Technological Advancement and Artificial Intelligence Methods: A Case Study of Management Ph.D. Researchers at Midocean University

This study aimed to assess the integration, ethical considerations, and governance of artificial intelligence (AI) within the PhD programs at Midocean University. It specifically sought to understand PhD researchers' perceptions and attitudes towards AI and identify areas for enhancement in AI-related policies and educational initiatives. A descriptive analytical approach was adopted, utilizing an electronic questionnaire distributed to 105 PhD researchers, with 54 completing the survey. The questionnaire was designed to measure various aspects of AI usage, ethical concerns, and governance practices. Statistical analysis was conducted to evaluate the relationships between AI awareness, ethical application, and governance perceptions. The findings revealed diverse perceptions of AI among researchers, indicating both opportunities and challenges in AI integration. The statistical analysis confirmed significant correlations between AI awareness and positive perceptions of ethical AI usage. However, the response rate and sample size posed limitations on the generalizability of the results. The study highlights the need for Midocean University to strengthen its AI governance frameworks and expand ethical guidelines to keep pace with technological advancements. Recommendations include enhancing AI-related educational programs, updating AI policies regularly, and promoting interdisciplinary research to foster an informed and ethically aware research community.

Investigating algorithm review boards for organizational responsible artificial intelligence governance

Investigating algorithm review boards for organizational responsible artificial intelligence governance