Artificial Intelligence Governance Research Articles

Unraveling generative AI in BBC News: application, impact, literacy and governance

Purpose This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies. Design/methodology/approach This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI. Findings By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI. Originality/value Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm.

Comparative technical analysis of legal and ethical frameworks in AI-enhanced procurement processes

This study presents a Comparative Technical Analysis of Legal and Ethical Frameworks in AI-Enhanced Procurement Processes. The research aims to evaluate the existing legal and ethical frameworks governing the use of artificial intelligence (AI) in procurement and to identify best practices for ensuring transparency, accountability, and ethical decision-making in AI-driven procurement processes. The study adopts a mixed-methods approach, combining quantitative surveys and qualitative interviews with procurement professionals and legal experts. The research design allows for a comprehensive analysis of the legal and ethical challenges associated with AI deployment in procurement and provides insights into practical strategies for addressing these challenges. Findings from the study indicate a growing recognition of the need for clear guidelines and regulations to govern the use of AI in procurement. While respondents acknowledge the potential benefits of AI, such as improved efficiency and cost savings, they also express concerns about algorithmic bias, data privacy, and the lack of transparency in AI-driven decision-making processes. Based on these findings, the study recommends several strategies for enhancing the legal and ethical frameworks in AI-enhanced procurement processes. These include developing clear guidelines for AI deployment, providing training and support for procurement professionals, and establishing mechanisms for monitoring and evaluating AI systems. Overall, the study highlights the importance of integrating legal and ethical considerations into AI deployment in procurement to ensure transparency, accountability, and ethical decision-making. The findings contribute to the growing body of literature on AI governance and provide practical insights for policymakers, procurement professionals, and other stakeholders involved in AI-driven procurement processes.

AI-driven Solutions for Cloud Compliance Challenges

The integration of artificial intelligence (AI) in cloud compliance management has emerged as a transformative approach to address complex regulatory challenges in contemporary IT environments. This research paper explores the role of AI-driven solutions in enhancing cloud compliance, focusing on key applications, benefits, challenges, and future trends. The paper begins by examining the automation of compliance reporting and documentation through AI technologies, highlighting significant improvements in accuracy, efficiency, and cost-effectiveness. Real-world case studies from leading organizations such as J.P. Morgan Chase & Co., Mayo Clinic, and Microsoft Corporation illustrate the practical implementation and impact of AI in various sectors, including finance, healthcare, and technology. Ethical and legal implications associated with AI in cloud compliance are discussed, emphasizing the importance of transparency, fairness, and regulatory adherence. Emerging trends such as Explainable AI (XAI), AI governance, and continuous compliance monitoring are identified as critical factors shaping the future of AI-driven cloud compliance. The paper concludes by addressing anticipated challenges in data privacy, algorithmic bias, and regulatory complexity, underscoring the need for collaborative efforts among stakeholders to foster responsible AI deployment in compliance operations.

Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

BackgroundThe ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.MethodsThe GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.ResultsWe describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.ConclusionsThe 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

A Study on the Risks and Countermeasures of False Information Caused by AIGC

Generative artificial intelligence (AIGC) has changed the traditional information production mechanism and has a wide range of application scenarios. At the same time, the security risks it exposes such as data leakage, false content generation, and improper utilization have also attracted widespread attention from various countries. The development, application and governance of AIGC no longer seem to be a common challenge faced by one country but by the entire international community. In order to effectively respond to the challenges of AIGC to the false information governance system, this article uses multiple methods such as literature analysis and in-depth research to elaborate on the potential risks of AIGC, and conducts an in-depth analysis of the global challenges of false information risk governance. Finally, Proposing governance paths and countermeasures from various perspectives such as supervision and ecology provides intelligence reference for the healthy development of the AIGC industry.

Plato’s Shadows in the Digital Cave: Controlling Cultural Bias in Generative AI

Generative Artificial Intelligence (AI) systems, like ChatGPT, have the potential to perpetuate and amplify cultural biases embedded in their training data, which are predominantly produced by dominant cultural groups. This paper explores the philosophical and technical challenges of detecting and mitigating cultural bias in generative AI, drawing on Plato’s Allegory of the Cave to frame the issue as a problem of limited and distorted representation. We propose a multifaceted approach combining technical interventions, such as data diversification and culturally aware model constraints, with a deeper engagement with the cultural and philosophical dimensions of the problem. Drawing on theories of extended cognition and situated knowledge, we argue that mitigating AI biases requires a reflexive interrogation of the cultural contexts of AI development and a commitment to empowering marginalized voices and perspectives. We claim that controlling cultural bias in generative AI is inseparable from the larger project of promoting equity, diversity, and inclusion in AI development and governance. By bridging philosophical reflection with technical innovation, this paper contributes to the growing discourse on responsible and inclusive AI, offering a roadmap for detecting and mitigating cultural biases while grappling with the profound cultural implications of these powerful technologies.

Global AI governance: barriers and pathways forward

Abstract Recent advances in artificial intelligence (AI), combined with a proliferation in use, have led to a newfound emphasis on strengthening the global governance of AI. In this article, we assess the prospects for stronger global AI governance and consider potential pathways forward. We map the nascent landscape of international regimes focused on AI governance and conclude that a governance deficit remains due to the inadequacy of existing initiatives, gaps in the landscape and difficulties reaching agreement over more appropriate mechanisms. First-order cooperation problems stemming from interstate competition and second-order cooperation problems from dysfunctional international institutions problematize overcoming current deficiencies in global AI governance. In light of these cooperation problems, we evaluate two pathways for strengthening global AI governance: (a) developing new centralized international AI institution(s); and (b) strengthening coordination between, and capacities of, existing institutions. We argue that strengthening the existing weak ‘regime complex’ of international institutions is the more politically legitimate and viable path forward. Improving coordination between, and capacities of, existing international institutions governing AI would support inclusive and mutually reinforcing policy change that can mitigate a range of risks associated with these technologies. As first steps for strengthening the weak AI regime complex, we recommend that the OECD is foregrounded as a centre of expert AI knowledge so that it can facilitate peer pressure among states and harmonization of policies. We also encourage scrutiny of different nodes in the regime complex to ensure that they are fulfilling appropriate functions based on their democratic mandates.

Applying the ethics of AI: a systematic review of tools for developing and assessing AI-based systems

Artificial Intelligence (AI)-based systems and their increasingly common use have made it a ubiquitous technology; Machine Learning algorithms are present in streaming services, social networks, and in the health sector. However, implementing this emerging technology carries significant social and ethical risks and implications. Without ethical development of such systems, there is the potential for this technology to undermine people’s autonomy, privacy, and equity, even affecting human rights. Considering the approaches necessary for ethical development and effective governance of AI, such as ethical principles, guidelines, and technical tools, the question arises regarding the limitations of implementing these measures by the highly technical personnel involved in the process. In this context, we propose the creation of a typology that distinguishes the different stages of the AI life-cycle, the high-level ethical principles that should govern their implementation, and the tools with the potential to foster compliance with these principles, encompassing both technical and conceptual resources. In addition, this typology will include relevant information such as developmental level, related tasks, sectors, and language. Our research is based on a systematic review in which we identified 352 resources and tools. We expect this contribution to be valuable in promoting ethical AI development for developers and leaders who manage these initiatives. The complete typology and the comprehensive list of resources are available for consultation at https://ricardo-ob.github.io/tools4responsibleai.

Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability

As artificial intelligence (AI) continues to permeate various aspects of our lives, the ethical challenges associated with its development become increasingly apparent. This paper navigates and reviews the ethical dilemmas in AI development, focusing on strategies to promote transparency, fairness, and accountability. The rapid growth of AI technology has given rise to concerns related to bias, lack of transparency, and the need for clear accountability mechanisms. In this exploration, we delve into the intricate ethical landscape of AI, examining issues such as bias and fairness, lack of transparency, and the challenges associated with accountability. To address these concerns, we propose strategies for transparency, including the implementation of Explainable AI (XAI), advocating for open data sharing, and embracing ethical AI frameworks. Furthermore, we explore strategies to promote fairness in AI algorithms, emphasizing the importance of fairness metrics, diverse training data, and continuous monitoring for iterative improvement. Additionally, the paper delves into strategies to ensure accountability in AI development, considering regulatory measures, ethical AI governance, and the incorporation of human-in-the-loop approaches. To provide practical insights, case studies and real-world examples are analyzed to distill lessons learned and best practices. The paper concludes with a comprehensive overview of the proposed strategies, emphasizing the importance of balancing innovation with ethical responsibility in the evolving landscape of AI development. This work contributes to the ongoing discourse on AI ethics, offering a roadmap for navigating the challenges and fostering responsible AI development practices.

Artificial intelligence development and dissemination impact on the sports industry labor market.

The objective of this study is to explore the impact of artificial intelligence (AI) development on the sports industry labor market, the ways in which AI has influenced the demand for labor, created new job opportunities, and impacted existing job roles. It refers to the inductive approach in the spirit technological determinism theory. It is based on the literature review and written qualitative, semi-structured interviews (N = 14) with sports human resources, management, and technology professionals (purposive sampling). Analysis involved inductive coding and line-by-line analytics of the data. The labor market implications of AI in the sports industry are multifaceted. New job roles are likely to emerge, demanding a blend of AI expertise, data-analysis skills, and sports domain knowledge. Professionals in roles such as sports data analysts and marketing experts may find increasing opportunities. However, certain jobs undergo transformation as AI automates routine tasks. It requires individuals to upskill or transition to roles that require a deeper understanding of AI. This necessitates the creation of responsibilities focused on ethical AI governance and oversight. It is important to research the impact of AI dissemination on the sports industry labor market in a holistic manner because the effects of AI are complex and far-reaching. While there are potential benefits to the implementation of AI, there are also potential risks and challenges that need to be addressed, the implementation of AI in the sports industry could have broader social and ethical implications that need to be considered.

Development and Threats of Artificial Intelligence in Industry and Workforce

The abstract explores the transformative impact of Artificial Intelligence (AI) on global industries, highlighting its role in enhancing efficiency and innovation while also posing challenges such as job automation, skills gaps, data misuse, and ethical concerns. Drawing from the Global Risk Report 2024 and recent regulatory actions by the European Union and Indonesia, the abstract discusses the pressing need for AI governance. However, it lacks specificity in articulating the study's objectives and scope, and could benefit from providing concrete examples or statistics to support its claims. A clearer organizational structure and citation of relevant sources would enhance the coherence and credibility of the abstract. Furthermore, while emphasizing the importance of human control over AI, the abstract could offer a more nuanced conclusion that underscores the significance of the study's findings in achieving this goal.

Towards experimental standardization for AI governance in the EU

The EU has adopted a hybrid governance approach to address the challenges posed by Artificial Intelligence (AI), emphasizing the role of harmonized European standards (HES). Despite advantages in expertise and flexibility, HES processes face legitimacy problems and struggle with epistemic gaps in the context of AI. This article addresses the problems that characterize HES processes by outlining the conceptual need, theoretical basis, and practical application of experimental standardization, which is defined as an ex-ante evaluation method that can be used to test standards for their effects and effectiveness. Experimental standardization is based on theoretical and practical developments in experimental governance, legislation, and innovation. Aligned with ideas and frameworks like Science for Policy and evidence-based policymaking, it enables co-creation between science and policymaking. We apply the proposed concept in the context of HES processes, where we submit that experimental standardization contributes to increasing throughput and output legitimacy, addressing epistemic gaps, and generating new regulatory knowledge.

SYNTHESIZING AI'S IMPACT ON CYBERSECURITY IN TELECOMMUNICATIONS: A CONCEPTUAL FRAMEWORK

As the telecommunications sector increasingly relies on interconnected digital infrastructure, the proliferation of cyber threats poses significant challenges to security and operational integrity. This review presents a conceptual framework for understanding and harnessing the potential of artificial intelligence (AI) in fortifying cybersecurity within the telecommunications industry. The framework integrates the transformative capabilities of AI with the unique demands of cybersecurity in telecommunications, aiming to enhance threat detection, mitigation, and response strategies. It encompasses a multidimensional approach that encompasses both technical and organizational facets, recognizing the interconnectedness of technology, human factors, and regulatory environments. Firstly, the framework delves into the application of AI in bolstering proactive threat intelligence gathering and analysis. Through advanced algorithms and machine learning techniques, AI empowers telecom operators to identify anomalous patterns, predict potential vulnerabilities, and pre-emptively adapt defensive measures. Secondly, it explores AI-driven solutions for dynamic risk assessment and adaptive cybersecurity protocols. By leveraging real-time data analytics and automated decision-making, telecom networks can swiftly adapt to evolving threats and ensure continuous protection against intrusions or breaches. Furthermore, the framework emphasizes the role of AI in augmenting human capabilities through intelligent automation and cognitive assistance. By offloading routine tasks and providing context-aware insights, AI enables cybersecurity professionals to focus on strategic initiatives and complex threat scenarios. Lastly, the framework addresses the imperative of ethical considerations, accountability, and transparency in deploying AI for cybersecurity in telecommunications. It advocates for responsible AI governance frameworks that prioritize privacy, fairness, and bias mitigation while fostering collaboration across industry stakeholders. In summary, this conceptual framework provides a roadmap for harnessing AI's transformative potential to fortify cybersecurity resilience in telecommunications, thereby safeguarding critical infrastructure and ensuring the integrity of global communication networks.
 Keywords: AI, Cybersecurity, Telecommunication, Framework, Conceptual, Impact, Review.

The Application of AI and Computer Science in the Context of International Law and Governance “Opportunities and Challenges”

Purpose: The purpose of "Governing Artificial Intelligence: Ethical, Legal, and Technical Opportunities and Challenges" is to examine the impact of artificial intelligence (AI) on various societal aspects and to underscore its potential benefits for human rights, social welfare, and economic growth. The essay emphasizes the necessity of regulating AI systems in a fair, transparent, and responsible manner, especially in high-risk sectors.
 Materials and Methods: Research Design: The essay employs a literature review and analysis approach to explore the ethical, legal, and technical challenges associated with governing AI.
 Method of Data Collection: Data collection primarily involves gathering information from existing literature, reports, and expert opinions. Analysis and Presentation: The data is analyzed qualitatively to provide insights into the complexities of AI governance. The findings are presented systematically to address different dimensions of the topic.
 Findings: The essay presents a comprehensive analysis of the ethical, legal-regulatory, and technological challenges in governing AI. It highlights the need for robust governance frameworks to ensure the responsible development and deployment of AI systems.
 Implication to Theory, Practice, and Policy: The study is informed by various theories on ethics, governance, and technology. Validation of these theories is achieved through a critical examination of existing literature and empirical evidence. Practitioners are recommended to adopt principles of fairness, transparency, and accountability in the development and deployment of AI systems. Additionally, continuous monitoring and evaluation mechanisms should be established to ensure compliance with ethical standards. Policymakers are encouraged to enact regulations that promote the ethical and responsible use of AI technologies. This includes establishing clear guidelines for AI development, deployment, and accountability mechanisms to address potential risks and ensure societal well-being.

Theoretical and Methodological Approaches to Studying Artificial Intelligence in the Context of International Relations and International Law

This article addresses one of the pressing issues regarding the role of artificial intelligence (AI) in international relations and international law. The research question revolves around defining the theoretical and methodological approaches applicable to the strategic analysis of AI utilization in these fields. In the contemporary world, there is a demand at both interstate and societal levels to define the role of AI in the political and legal spheres. This is because AI development affects crucial areas of state relations such as security, international law, ethical norms, and dependencies. The prospective use of AI technologies without corresponding legal regulation may disrupt the already fragile balance of the world order, which could be exacerbated by state competition in AI technologies and AI applications in the military domain, a grey area in international law. Analyzing this issue from the perspective of international relations and international law theory allows for examining AI's impact on state interactions and developing new application strategies. Similarly, it helps understand how international law regulates state relations, including aspects related to AI applications. By examining various theoretical concepts and methodological approaches necessary for understanding AI's impact on global affairs, including its influence on diplomacy, security, and governance structures, as well as legal and ethical issues, this article contributes to Kazakhstan's evolving discourse on AI governance and its implications for state actors.

HOW TO STRENGTHENING OR WEAKENING THE PROCEDURES OF CORPORATE GOVERNANCE: AN ARTIFICIAL INTELLIGENCE PERSPECTIVE

This research aims to examine the impact of artificial intelligence (AI) on corporate governance practices in non-financial enterprises in Qatar. It explores how AI can either strengthen or weaken these practices and provides suggestions for integrating AI into corporate governance. The study utilizes case studies, actual data, and existing literature, along with a non-interview-based methodology, to analyze the connection between corporate governance and AI. Through synthesis and comparison, this research offers a comprehensive examination of the subject. It acknowledges the unique challenges and opportunities faced by non-financial organizations in Qatar when implementing AI for corporate governance. The insights provided in this research are relevant not only to similar businesses in other locations but also highlight the importance of incorporating AI into corporate governance procedures. The findings emphasize the need for businesses to invest in AI technology and for legislators to establish supportive laws for AI's application in corporate governance. This study stands out from previous research by focusing on the relationship between AI and corporate governance and highlighting the role of AI in enhancing business performance. The conclusions drawn from this study are valuable for regulators, legislators, and businesses seeking to leverage AI for improved corporate governance.

Advancements and Applications of Generative Artificial Intelligence and Large Language Models on Business Management: A Comprehensive Review

This comprehensive review delves into the landscape and recent advancements of Generative Artificial Intelligence (AI) and Large Language Models (LLMs), shedding light on their transformative potential and applications across various sectors. Generative AI, exemplified by models like ChatGPT, DALL-E, and Midjourney, has rapidly evolved and is driven by breakthroughs in deep learning architectures and the availability of vast datasets. Concurrently, LLMs have revolutionized natural language processing tasks, utilizing vast text corpora to generate human-like text. The study explores recent developments, including the introduction of advanced models like GPT-4 and PaLM2 and the emergence of specialized LLMs like small LLMs (sLLMs), aimed at overcoming hardware limitations and cost constraints. Additionally, the expanding applications of generative AI, from healthcare to finance, underscore its transformative potential in addressing real-world challenges. Through a comprehensive analysis, this research contributes to the ongoing discourse on AI ethics, governance, and regulation, emphasizing the importance of responsible innovation for the benefit of humanity.

The impact of artificial intelligence (AI) on employment and economic growth in BRICS: Does the moderating role of governance Matter?

The BRICS countries, namely Brazil, Russia, India, China, and South Africa, are committed to achieving and sustaining Sustainable Development Goal (SDG) 8 of the United Nations, which focuses on sustainable economic growth and productive employment and decent work for all. However, the empirical research question on how to effectively harness the use of artificial intelligence (AI) in the presence of governance dynamics is yet to be addressed in the literature, particularly concerning the BRICS economy. Hence, it is crucial to examine the impact of AI investment on employment and economic growth in the BRICS countries from 2012 to 2022, taking into account the moderating role of overall governance as well as specific governance indicators such as political governance, institutional governance, economic governance etc. Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL) technique is used to analyze the data. The findings of the study suggest a long-run equilibrium relationship among the variables analyzed in both the employment and growth models. The causality results for our main variables of interest differ in the employment-growth models. Based on the CS-ARDL results, the study recommends that BRICS governments and policymakers prioritize and enhance the integration of AI into their governance systems to promote employment and stimulate growth in both the short- and long-term. However, the study cautions against overlooking interaction between AI and general governance as it did not demonstrate support for growth, it is crucial to implement robust measures to mitigate any potential negative effects arising from AI's interaction with political and institutional governance. Therefore, the study proposes the formulation of AI-friendly governance policies among BRICS nations to stimulate employment and growth, recognizing the embryonic status of AI as a pivotal technology in the Fourth Industrial Revolution.

The future of AI politics, policy, and business

AbstractOur aim with this special issue on the future of artificial intelligence (AI) politics, policy, and business is to give space to considering how the balalnce between risk and reward from AI technologies is and perhaps should be pursued by the public and private sectors. Ultimately, private firms and regulators will need to work collaboratively, given the complex networks of actors involved in AI development and deployment and the potential for the technology to alter existing policy regimes. We begin the introduction of this special issue of Business & Politics with a discussion of the growth in AI technology use and discussions of appropriate governance, followed by a consideration of how AI-related politics, policy, and business intersect. We then summarize the contributions of the authors in this issue and conclude with thoughts about how political science, public administration, and public policy scholars have much to offer, as well as much to study, the establishment of effective AI governance.

Navigating Cyber Diplomacy in the Governance of Emerging AI Technologies: Lessons from Transatlantic Cooperation

The rise of Artificial Intelligence (AI) technology presents vast transformative possibilities across various sectors, encompassing economic, industrial, social, political, intelligence, and military realms. Consequently, governing the development and deployment of AI has garnered significant attention not only from policymakers and decision-makers but also from the general public. Given AI's potential to shape state power and its dual strategic applications, the governance of AI has become an integral part of global discussions, falling under the purview of cyber diplomacy. This article delineates key issues surrounding AI governance, discusses the evolving role of the EU as a normative force in this arena, and underscores the importance of transatlantic collaboration amid broader global technological competitions.