Government Intelligence Research Articles

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

LEGAL ANALYSIS OF EU ARTIFICIAL INTELLIGENCE ACT (2024): INSIGHTS FROM PERSONAL DATA GOVERNANCE AND HEALTH POLICY

Background: This study correlates the up-to-date ethical, functional and legal evaluations related to the management and governance of artificial intelligence (AI) under European Union (EU) law, particularly impacting the health data sector and medical standards as provided by the Artificial Intelligence Act within the Regulation adopted by the European Council in May 2024. The initial proposal for the management and governance of the AI sector was submitted in April 2021. Three years later, on 13 March 2024, the European Union Artificial Intelligence Act (EU AIA) was adopted by the European Parliament. Subsequently, on 21 May 2024, the Council adopted an innovative legislative framework that harmonises the standards and rules for AI regulation. This framework is set to take effect in May 2026, with the central objective of stimulating and motivating a fair, safe, legal single market that respects the principles of ethics and the fundamental rights of the human person. Methods: The current legal analysis focuses on the European Union’s new institutional governance involving a multistage approach to managing health data, ethical artificial intelligence, generative artificial intelligence and classification of types of AI by considering the degree of risk (e.g. artificial intelligence systems with limited risk and systems with high risk) and medical devices. It outlines the legal framework for AI regulation and governance in the EU by focusing on compliance with the previously adopted legislation in the Medical Devices Regulation (2017) and the In-Vitro Diagnostic Regulation (2017). The paper also examines the application of the newly adopted EU Artificial Intelligence Act in relation to national justice systems, previous EU regulations on medical devices and personal data protection regulation, and its correlation with the European Court of Human Rights jurisprudence. This opens up complex discussions related to judicial reform and access to justice. For this purpose, as a research objective, the legal analysis includes an innovative perspective following an integrative discussion on the latest legal reforms and regulations of the AI sector in Eastern Europe launched in 2024 with a special focus on the latest developments in the EU Candidate Countries namely Ukraine and the Republic of Moldova. Results and conclusions: The present research facilitates the exploration of the real benefits of managing innovative AI systems for medical data, research, and development, as well as within the medical technology industry.

Ethics and Governance of Artificial Intelligence for Health and Current Situation in Taiwan

<p>現代人工智慧(artificial intelligence，以下簡稱為AI)從數據中學習後並將演算法整合到系統與工具中，使其能夠自動執行任務，讓人類不須再對每一個步驟進行明確的編寫程式。生成式AI為一類AI技術，其演算法被訓練能生成具有原創性的內容，例如文章、圖像或影片等等。世界衛生組織(WHO)首次在2021年6月底公布了一份全面指南：「人工智慧於健康領域之倫理與治理」(Ethics and governance of artificial intelligence for health)。WHO諮詢了20位AI領域頂尖專家並指出了在醫療保健中使用AI的潛在好處和風險，並提出了六項經過共識達成的原則供政府、研究者與醫療提供者在使用AI時參考。這些原則包括：(1)保護人類自主權；(2)促進人類福祉、安全、和公共利益；(3)確保透明度、可解釋性、和可理解性；(4)確立責任歸屬和問責制；(5)確保包容性和公平性；(6)促進具適應性及可持續之人工智慧。</p> <p>WHO於2024年1月再次更新本指南並探討了生成式AI，即大型多模態模型(Large Multimodal Models, LMMs)，其能接受多種類型的數據輸入並生成多樣的輸出。有研究預測稱LMMs將在醫療保健、科學研究、公共衛生和藥物開發中得到廣泛應用、亦被稱為「通用目的基礎模型」。儘管尚未證明LMMs是否能夠勝任各種程度的工作，不可否認的是其被廣泛應用的速度比歷史上任何工具都更快。LMMs的吸引力在於其透過模仿人類溝通的方式促進了「人機互動」，並在詢問或數據輸入後生成「看似人類」或甚至「權威般」的回應。由於其快速地被大眾接受並使用，以及其潛在顛覆傳統社會服務模式的能力，許多政府機關及企業政府均積極投入發展生成式AI。</p> <p>WHO發布這份指南，旨在幫助評估使用LMMs在醫療保健方面的權益和挑戰，並制定相對應的政策。本文將簡介這篇指南中關於在醫療領域使用AI的建議與利弊，並討論現今歐盟、美國及台灣在AI領域相關法令以及發展。</p> <p> </p><p>Modern AI (artificial intelligence) learns from data and automates tasks, while generative AI creates original content like articles, images, or videos. WHO’s comprehensive guideline “Ethics and Governance of Artificial Intelligence for Health” released in June 2021 outlines six principles for AI applications in healthcare, including protecting human autonomy and promoting transparency. The principles aim to guide governments, researchers, and healthcare providers in utilizing AI responsibly. </p> <p> In January 2024, WHO updated its guidelines, focusing on generative AI, particularly Large Multimodal Models (LMMs), which is capable of producing diverse outputs from various data inputs. Despite uncertainties regarding their capabilities, research suggests that LMMs will have broad applications in healthcare and beyond, known as “general-purpose baseline models.” Their rapid adoption is attributed to their ability to mimic human interaction, potentially disrupting traditional social services, leading to significant investments from governments and enterprises. </p> <p> WHO releases this guideline to assist in assessing the benefits and challenges of using LMMs in healthcare and to formulate corresponding policies. This article provides an overview of the recommendations and the pros and cons of using AI in the medical field outlined in this guideline, as well as discuss the current EU, US, and Taiwan regulations and developments in the field of AI.</p> <p> </p>

Toward a Policy Approach to Normative Artificial Intelligence Governance: Implications for AI Ethics Education

Toward a Policy Approach to Normative Artificial Intelligence Governance: Implications for AI Ethics Education

Decoding India’s AI Governance Strategy and its Implications for the U.S.-India Bilateral Relationship

India is developing a unique approach to the governance of artificial intelligence (AI) and is staking out a leadership role in multilateral dialogues on responsible AI. This paper reviews India’s available AI governance choices, as the country defines its regulatory model, and highlights their associated trade-offs. It also analyses these choices in light of the government’s national priorities, as well as India’s perceived advantages and weaknesses in various industries that might utilize AI systems. With this background, this paper examines how India’s current approach to AI governance may impact the trajectory of U.S.-India technology cooperation.

Environmental, Social, and Governance-Based Artificial Intelligence Governance: Digitalizing Firms’ Leadership and Human Resources Management

The integration of artificial intelligence (AI) with environmental, social, and governance (ESG) factors is impacting the direction of enterprises and society in our swiftly expanding world. This collaboration has significant potential to tackle critical issues such as reducing the impact of climate change, fostering social integration, and improving corporate governance. Nevertheless, the implementation of AI gives rise to intricate matters and apprehensions, as it brings out a distinct array of hazards and ethical quandaries for ESG performance. The objective of the present research is to fill this gap by gathering and offering a contemporary evaluation of the influence of advancing technologies on the strategic leadership’s role in fulfilling the business goal within the context of ESG considerations. We used bibliometric analysis to investigate the study subject using R Studio version 4.2.0 and the bibliometric applications VOSviewer version 1.6.20 and Biblioshiny version 4.2.0. We obtained data from the Scopus database and used the PRISMA approach to suitably choose 205 research publications. The results suggest that it is essential to use AI and ESG to digitize the boardroom. Additionally, it is crucial to guarantee its security using an advanced detection system. Therefore, chief executive officers (CEOs) must give priority to the issues of transparency and cybersecurity to reduce risks and successfully inspire trust in business activities.

Decolonizing global AI governance: assessment of the state of decolonized AI governance in Sub-Saharan Africa.

Global artificial intelligence (AI) governance must prioritize equity, embrace a decolonial mindset, and provide the Global South countries the authority to spearhead solution creation. Decolonization is crucial for dismantling Western-centric cognitive frameworks and mitigating biases. Integrating a decolonial approach to AI governance involves recognizing persistent colonial repercussions, leading to biases in AI solutions and disparities in AI access based on gender, race, geography, income and societal factors. This paradigm shift necessitates deliberate efforts to deconstruct imperial structures governing knowledge production, perpetuating global unequal resource access and biases. This research evaluates Sub-Saharan African progress in AI governance decolonization, focusing on indicators like AI governance institutions, national strategies, sovereignty prioritization, data protection regulations, and adherence to local data usage requirements. Results show limited progress, with only Rwanda notably responsive to decolonization among the ten countries evaluated; 80% are 'decolonization-aware', and one is 'decolonization-blind'. The paper provides a detailed analysis of each nation, offering recommendations for fostering decolonization, including stakeholder involvement, addressing inequalities, promoting ethical AI, supporting local innovation, building regional partnerships, capacity building, public awareness, and inclusive governance. This paper contributes to elucidating the challenges and opportunities associated with decolonization in SSA countries, thereby enriching the ongoing discourse on global AI governance.

Artificial Intelligence Governance

Research on AI governance is important towards potentially useful and constraining affordable misuse, reduce new risks and economic trends that threaten to disrupt public political and economic trends, and drive off target as interest in advanced AI systems and the norms, focal points, and use of new AI research are potentially transformative and governance institutions aim to prevent. Potential public benefits from policy community re-using AI research are enormous, including reduced economic instability. A fundamental challenge in AI governance is a cognitive framing challenge: governing AI research requires understanding new kinds of safety risks, performance goals, and intended applications that advanced AI systems will make possible. Specifically, the letter focuses on how AI research could mitigate issues such as the possibility of AI capabilities getting concentrated within a small and hard-to-regulate group of actors, and ultimately recommends the prioritization of open research and collaboration, with concern for long-term social and economic looming to the forefront of coalitions if AI becomes an increasingly important aspect of the future economy and society.

It's time for artificial intelligence governance

Advances in Internet of Things and Artificial Intelligence have transformed society with the potential to foster the prosperity of human beings and improving societal welfare. However, at the same time, there are concerns about their potential negative impact. In this regard, it is of utmost importance to establish a regulatory framework for the Artificial Intelligence and the Internet of Things that will ensure that human rights and fundamental freedoms are respected, promoted, and protected.In this sense, the goal of this article is to propose the Artificial Intelligence and the Internet of Things Governance model. To achieve a better regulation concept, for the Regulatory Impact Assessment we used the Analytical Hierarchy Process method.

Enhancing ethical codes with artificial intelligence governance – a growing necessity for the adoption of generative AI in counselling

ABSTRACT The advent of generative Artificial Intelligence (AI) systems, such as large language model chatbots, is likely to have a significant impact in psychotherapy and counselling in the future. In this paper we consider the current state of AI in psychotherapy and counselling and the likely evolution of this field. We examine the ethical codes of practice for counselling in four countries in different parts of the world, namely the UK, the USA, Australia and Malaysia, and identify aspects of these codes that will need enhancement to reflect good AI governance. Using the Model Artificial Intelligence Governance Framework as an example, we have identified how the key elements of the AI framework relate to the core elements of the ethical codes, as a pointer to how such ethical codes will need to be enhanced if generative AI systems are to be adopted by the counselling profession.

Governance fix? Power and politics in controversies about governing generative AI

Abstract The launch of ChatGPT in late 2022 led to major controversies about the governance of generative artificial intelligence (AI). This article examines the first international governance and policy initiatives dedicated specifically to generative AI: the G7 Hiroshima process, the Organisation for Economic Cooperation and Development reports, and the UK AI Safety Summit. This analysis is informed by policy framing and governance literature, in particular by the work on technology governance and Responsible Innovation. Emerging governance of generative AI exhibits characteristics of polycentric governance, where multiple and overlapping centers of decision-making are in collaborative relationships. However, it is dominated by a limited number of developed countries. The governance of generative AI is mostly framed in terms of the risk management, largely neglecting issues of purpose and direction of innovation, and assigning rather limited roles to the public. We can see a “paradox of generative AI governance” emerging, namely, that while this technology is being widely used by the public, its governance is rather narrow. This article coins the term “governance fix” to capture this rather narrow and technocratic approach to governing generative AI. As an alternative, it suggests embracing the politics of polycentric governance and Responsible Innovation that highlight democratic and participatory co-shaping of technology for social benefit. In the context of the highly unequal distribution of power in generative AI characterized by a high concentration of power in a small number of large tech companies, the government has a special role in reshaping the power imbalances by enabling wide-ranging public participation in the governance of generative AI.

Transcending Carbon Life: A Vision for Sustainable Future

Abstract The concept of “transcending carbon life” proposes a shift from traditional carbon-based biological existence to a data-centric, digital form of life. This is driven by the need to address escalating environmental crises caused by carbon-intensive human activities. The main challenges of carbon-based life include climate change, resource depletion, and environmental degradation. “Transcending Carbon Life” aims to address these issues by leveraging advancements in digital technologies and data science. The core principles involve digital existence, where life forms could be replicated and preserved as data, and data-centric living, emphasizing the use of high-quality data and synthetic data to drive decision-making and innovation. Potential benefits include improved environmental sustainability, enhanced medical research and life extension, and better global connectivity and knowledge sharing. However, significant technological development, ethical considerations, and infrastructure challenges need to be addressed to make this vision a reality. Overall, “transcending carbon life” represents a transformative approach to ensuring the long-term survival, superior intelligence governance, and ecological balance of all living entities on earth.