Artificial Intelligence Policy Research Articles

The making of government-business relationships through state rescaling: a policy analysis of China’s artificial intelligence industry

ABSTRACT Developing artificial intelligence (AI) is a priority on China’s state agenda, yet the constitutive state roles in AI development are understudied. Against the background of the coexistence of authoritarianism and market liberalism in the governance of the Chinese economy, state rescaling is a useful lens to understand how China is developing this new strategic sector. This paper proposes an analytical framework reifying vertical and horizontal scalar relations via the rescaling lens and three state roles (owner, promoter, and supervisor) to explore how government-business relationships are made. More than 100 Chinese AI policy documents have been collected from central, provincial, and city levels for a systematic multi-scalar policy analysis. As a result, this paper captures both downscaling/upscaling within the state hierarchy and statization/destatization between state and non-state actors in China’s AI development. A series of intertwined state rescaling practices manifest themselves in the state roles figuring in three functionalities of government-business relationships (sponsorship, cultivation, and disciplining). It is argued that the state roles are not pre-given modalities in a setting of a specific industry. The restructuring and even revolutionizing effects of AI in the socio-economic systems prompt non-state actors to respond proactively, which shapes the variegated functionalities of government-business relationships.

Examining AI Guidelines in Canadian Universities: Implications on Academic Integrity in Academic Writing

Academic integrity is a crucial aspect of higher education that fosters intellectual honesty and upholds the principles of fairness and trustworthiness (Stoez & Eaton, 2020; Kang, 2022; Eaton, 2022). As the introduction and integration of artificial intelligence (AI) technologies becomes increasingly prevalent in educational settings, it is imperative to examine how Canadian universities are addressing the implications of AI on academic integrity (Eaton, 2022; UNESCO, 2023). This study aimed to examine the existing AI guidelines and policies developed and implemented by Canadian universities and analyze their alignments and gaps in relation to their academic integrity policies, particularly in the domain of academic writing in Canadian higher education contexts. In this research study, sixteen Canadian universities were selected for document analysis, and through an examination of their existing polices and guidelines on AI, results revealed insights into both challenges and opportunities for faculty, students and stakeholders around teaching academic writing while upholding academic integrity in higher education.

AI’s effect on innovation capacity in the context of industry 5.0: a scoping review

The classic literature about innovation conveys innovation strategy the leading and starting role to generate business growth due to technology development and more effective managerial practices. The advent of Artificial Intelligence (AI) however reverts this paradigm in the context of Industry 5.0. The focus is moving from “how innovation fosters AI” to “how AI fosters innovation”. Therefore, our research question can be stated as follows: What factors influence the effect of AI on Innovation Capacity in the context of Industry 5.0? To address this question we conduct a scoping review of a vast body of literature spanning engineering, human sciences, and management science. We conduct a keyword-based literature search completed by bibliographic analysis, then classify the resulting 333 works into 3 classes and 15 clusters which we critically analyze. We extract 3 hypotheses setting associations between 4 factors: company age, AI maturity, manufacturing strategy, and innovation capacity. The review uncovers several debates and research gaps left unsolved by the existing literature. In particular, it raises the debate whether the Industry5.0 promise can be achieved while Artificial General Intelligence (AGI) remains out of reach. It explores diverging possible futures driven toward social manufacturing or mass customization. Finally, it discusses alternative AI policies and their incidence on open and internal innovation. We conclude that the effect of AI on innovation capacity can be synergic, deceptive, or substitutive depending on the alignment of the uncovered factors. Moreover, we identify a set of 12 indicators enabling us to measure these factors to predict AI’s effect on innovation capacity. These findings provide researchers with a new understanding of the interplay between artificial intelligence and human intelligence. They provide practitioners with decision metrics for a successful transition to Industry 5.0.

Prospects for the Convergence of the Legislation of the EAEU Countries Regarding the Legal Regulation of Artificial Intelligence

This study is devoted to the analysis of the prospects for the convergence of the legislation of the EAEU countries regarding the legal regulation of artificial intelligence (AI).Aim. To identify the need and identify the prerequisites for supranational legal regulation of AI in the EAEU. Tasks. To list the features of AI that necessitate supranational legal regulation in the context of analyzing the consequences for the purposes of the EAEU. To make a classification and analyze the prerequisites for the formation of the will of the EAEU member states to bring together the legislation of the EAEU countries in terms of legal regulation of AI.Methods. The problem-theoretical, formal-legal, logical, system-structural method and the method of comparison are used.Results. The study showed that such features of AI technologies as the ability to cause cross-border harm and the ability to autonomous processes require: a) the establishment of legal limits for delegating human authority to a machine that are uniform for the EAEU states, which is achieved by establishing administrative responsibilities for participants in the life cycle of AI systems and applications; b) developing a unified approach to eliminating the “responsibility gap” for the harm caused by AI and its legal consolidation in the law of the EAEU, which is achieved through supranational regulation of AI on these issues. The lack of “uniform norms” regarding the distribution of responsibility for harm produces legal conflicts that contribute to the creation of obstacles to the functioning of internal markets and asymmetries in the development of AI within the EAEU. The results of the analysis of the prerequisites for the formation of the will of the EAEU member states to bring together the legislation of the EAEU countries in terms of legal regulation of AI allow us to state the absence of prerequisites for a unified policy of the EAEU countries in the creation and use of AI, and consequently, the prospects for the convergence of legislation in the field of public relations through the formation of supranational legal regulation. However, the EAEU law does not contain obstacles to the implementation of a unified AI policy in the future.Conclusions. The specifics of AI technologies require supranational legal regulation of AI, at least in matters of allocation of responsibility for harm caused by AI in order to avoid legal conflicts that contribute to the creation of obstacles to the functioning of internal markets and asymmetries in the development of AI within the EAEU. Despite the current lack of prerequisites, the EAEU law does not contain obstacles to the convergence of the legislation of the EAEU countries in terms of legal regulation of AI in the event of such a decision.

What problems is the AI act solving? Technological solutionism, fundamental rights, and trustworthiness in European AI policy

ABSTRACT Artificial intelligence (AI) policy has been a key topic in public and political discourse for some time. Mobilizing Bacchi’s ‘What’s the problem represented to be?’ framework, we critically explore the policy responses to AI presented by the European Commission, the problem representations that are assumed within and justify these particular responses, and the wider effects of such problematizations. We find that EU AI policy constitutes the technology as both opportunity and threat, and that these problematizations demand a specific policy response – the risk-based categorizations of the AI Act – that allows these different imaginations of AI to cohere. In addition, we argue that one effect of these problematizations is the enactment of particular versions of Europe and Europeanness. In proposing a specific policy solution, European AI policy enacts not only AI but an exceptionalist notion of Europe as a policy actor and coherent political community.

Media framing of artificial intelligence: an analysis of framing in Romanian online publications

Artificial intelligence (AI) is increasingly at the center of public discourse. Considering the current importance of AI technologies, but also the influence that the mass media has on users' adoption of new technologies and their regulation, the article aims to analyze the attitudes of online publications in Romania towards AI. By using content analysis, the attitudes and themes of 100 articles selected from the most visited Romanian news websites were examined. The results reveal a relatively balanced distribution of attitudes, with 37% of articles expressing negative sentiments, 35% positive and 28% neutral. The most frequently addressed topics include the impact on jobs, especially the negative one, regulations in the field and new functionalities. Compared to the publications in English, as can be seen from the specialized literature, the Romanian articles present a more balanced attitude towards technology, with less emphasis on the dangers. However, the analysis shows that current events can strongly influence public discourse, and approaches vary by publication. These findings suggest a diverse debate, subject to rapid changes, with important implications for AI communication, education and policy in the Romanian space.

Data justice in the “twin objective” of market and risk: How discrimination is formulated in EU's AI policy

AbstractBased on a focus on artificial intelligence (AI) policy in the European Union (EU), we explore the dominant approach taken to data justice in policy. More specifically, we ask how the particular issue of discrimination is translated into policy goals and measures as a way to address prominent concerns about AI. Looking at the stage of policy formulation, we provide an analysis of the way (non) discrimination is currently pursued within the EU's AI policy debate through the study of relevant policy documents and public consultations between 2017 and 2023. We argue that whilst the issue of discrimination has moved from the margins to the mainstream in policy debate, it has done so based on an understanding of discrimination as an inevitable risk of AI; such risk is specific to particular situations and the technological features of AI; the nature of this risk can be assessed and managed through a set of procedural safeguards; and such safeguards can be supported by the creation of a trustworthy AI market. Whilst this translation of justice is very important for contending with some of the critique surrounding the advancement of AI, it may also serve to contain and neutralize such critique in the interest of marketization.

Ethics of Artificial Intelligence: Dialectics of Artificial Intelligence Policy for Humanity

Artificial Intelligence is now widely used by humans. The use of this technology is based on the view that Artificial Intelligence can make their lives easier. Many sectors have utilized this technology, including government, private, social, health, to education. Even though Artificial Intelligence is felt to have many benefits, there are perceived threats so that appropriate policies are needed. Thus, the aim of this research is to find out policies that can be recommended for the use of Artificial Intelligence that focus on humanitarian aspects. This research uses a qualitative approach to deepen the literature review that has been carried out. The results of this research show that the presence of Artificial Intelligence provides quite large benefits, especially as a technology for predicting the future. However, to regulate the use of this technology, appropriate policies are needed to avoid increasingly widespread digital crimes. In formulating Artificial Intelligence policies, humanitarian aspects need to be considered to provide appropriate protection. Especially for vulnerable groups who have low access to the use of Artificial Intelligence.

Application of Artificial Intelligence in Chinese Education based on Bibliometric Analysis

In the last decade, artificial intelligence (AI) has been widely used in education in China. Many scholars have carried out research on the theory of the integration of AI and education and have produced a lot of achievements. In this paper, CiteSpace software is used to visually analyze the research status, progress, hotspot and trend of the application of AI in education in China from the core database of China National Knowledge Net. According to the analysis of the results, it is suggested that educational AI policy and future research should be constructed mutually.AI in education co-constructs a three-dimensional pattern of researchers, research institutions and policy promoters; Focus on the high-quality, scientific and cross-field comprehensive development of AI in education field; Educational AI pays attention to the diversified research of quality combination.

Dynamics of the Digital Workforce: Assessing the Interplay and Impact of AI, Automation, and Employment Policies

The rapid integration of artificial intelligence (AI) and automation within various sectors poses challenges and opportunities for the global workforce. This study investigates the implications of AI and automation on employment patterns, skills requirements, and remote work infrastructures. Employing a quantitative research design, data was collected through a structured questionnaire administered to 482 professionals across the information technology, healthcare, and finance sectors. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test hypotheses related to the impact of technological advancements on employment. Major findings indicate a significant, albeit complex, impact of AI and automation on employment. Most respondents recognized AI and automation as catalysts for creating new job opportunities and enhancing productivity, particularly in sectors with high integration of digital technologies. However, the study also highlighted substantial concerns regarding the widening skills gap and the adequacy of current employment policies in managing the transition. Specifically, sixty-nine percent of respondents identified a significant skills gap necessitating urgent educational and training interventions. About half of the respondents viewed existing employment policies as inadequate in addressing the challenges of rapid technological changes. The study concludes that while AI and automation are reshaping the employment landscape, creating new types of jobs, and altering skill requirements, there is a critical need for proactive adaptation strategies. Recommendations include developing targeted reskilling programs, adaptive employment policies, and robust remote work infrastructures to support an increasingly digital workforce. These strategies are essential to harness the benefits of digital transformations while mitigating potential adverse effects on employment.

Framing contestation and public influence on policymakers: evidence from US artificial intelligence policy discourse

Abstract As artificial intelligence (AI) policy has begun to take shape in recent years, policy actors have worked to influence policymakers by strategically promoting issue frames that define the problems and solutions policymakers should attend to. Three such issue frames are especially prominent, surrounding AI’s economic, geopolitical, and ethical dimensions. Relatedly, while technology policy is traditionally expert-dominated, new governance paradigms are encouraging increased public participation along with heightened attention to social and ethical dimensions of technology. This study aims to provide insight into whether members of the public and the issue frames they employ shape—or fail to shape—policymaker agendas, particularly for highly contested and technical policy domains. To assess this question, the study draws on a dataset of approximately five million Twitter messages from members of the public related to AI, as well as corresponding AI messages from the 115th and 116th US Congresses. After using text analysis techniques to identify the prevalence of issue frames, the study applies autoregressive integrated moving average and vector autoregression modeling to determine whether issue frames used by the public appear to influence the subsequent messaging used by federal US policymakers. Results indicate that the public does lead policymaker attention to AI generally. However, the public does not have a special role in shaping attention to ethical implications of AI, as public influence occurs only when the public discusses AI’s economic dimensions. Overall, the results suggest that calls for public engagement in AI policy may be underrealized and potentially circumscribed by strategic considerations.

Impact of Artificial Intelligence on New Global Order: A Nepalese Security Perspective

The novelty of Artificial Intelligence (AI) and nascent geo-tech interests of powerful countries have largely influenced foreign policy, while ‘techno-geopolitics’ and the emergence of the ‘AI world order’ have constantly challenged the world order milieu. The changing global power dynamics, including the escalating Russia-Ukraine war, the Israel-Palestine conflict and China’s rising clout in tech and diplomatic spheres have induced specific geo-political challenges to the US-led global order. In this context, this research primarily unfolds whether the remaking of new global order can fundamentally be signified by the end of bipolar or unipolar world order, while the advancement of AI technology and geo-tech interests of tech powers have contributed to a remaking of new global order. As the powerful countries have fundamentally concentrated on marshaling AI in foreign policy, both AI and foreign policy have been closely interlinked. This research aims to explore the impact of AI on new global order and corresponding security concerns, particularly Nepalese security concerns. Since both AI and the new global order are relatively vast fields, this research focuses on tech foreign policy that is directly linked with the balance of power and the corresponding international order. This study adopts an analytical descriptive research method. It relates AI ethics and global tech concerns, considering the global need, beginning with the notion of multilateral tech diplomacy, and inquiring whether the tech foreign policy is truly functional. Despite varying challenges to the new global order, rational ‘geopolitical balancing’ and techno-economic cooperation in ‘better-functioning relations’ with immediate neighbors and other superpowers drive Nepal’s security architecture.

Investigating the politics and content of US State artificial intelligence legislation

AbstractThe rapid emergence of artificial intelligence (AI) technology and its application by businesses has created a potential need for governmental regulation. While the federal government of the United States has largely sidestepped the issue of crafting law dictating limitations and expectations regarding the use of AI technology, US state legislatures have begun to take the lead in this area. Nonetheless, we know very little about how state legislatures have approached the design, pursuit, and adoption of AI policy and whether traditional political fault lines have manifested themselves in the AI issue area. Here, we gather data on the state-level adoption of AI policy, as well as roll call voting on AI bills (classified on the basis of consumer protection versus economic development), by state legislatures and analyze the political economy of AI legislation. We find that rising unemployment and inflation are negatively associated with a state’s AI policymaking. With respect to individual legislator support, we find that liberal lawmakers and Democrats are more likely to support bills establishing consumer protection requirements on AI usage. The results suggest that economic concerns loom large with AI and that traditional political fault lines may be establishing themselves in this area.

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.

Mapping Ethical Artificial Intelligence Policy Landscape: A Mixed Method Analysis

As more national governments adopt policies addressing the ethical implications of artificial intelligence, a comparative analysis of policy documents on these topics can provide valuable insights into emerging concerns and areas of shared importance. This study critically examines 57 policy documents pertaining to ethical AI originating from 24 distinct countries, employing a combination of computational text mining methods and qualitative content analysis. The primary objective is to methodically identify common themes throughout these policy documents and perform a comparative analysis of the ways in which various governments give priority to crucial matters. A total of nineteen topics were initially retrieved. Through an iterative coding process, six overarching themes were identified: principles, the protection of personal data, governmental roles and responsibilities, procedural guidelines, governance and monitoring mechanisms, and epistemological considerations. Furthermore, the research revealed 31 ethical dilemmas pertaining to AI that had been overlooked previously but are now emerging. These dilemmas have been referred to in different extents throughout the policy documents. This research makes a scholarly contribution to the expanding field of technology policy formulations at the national level by analyzing similarities and differences among countries. Furthermore, this analysis has practical ramifications for policymakers who are attempting to comprehend prevailing trends and potentially neglected domains that demand focus in the ever-evolving field of artificial intelligence.

EU AI sovereignty: for whom, to what end, and to whose benefit?

ABSTRACT EU policy emphasises the need for digital sovereignty, also in artificial intelligence (AI). But because such ‘AI sovereignty’ could be used for diverse and even conflicting goals, it obscures the tensions that actual EU AI strategy entails. Conceptually, this article proposes three central trade-offs: first, does AI sovereignty pit the EU against other major AI powers, or rather citizens against large tech companies? Second, is AI sovereignty meant to boost the EU’s position in a putative AI race, or is it a means to defy this competitiveness-logic, instead? And third, is EU AI sovereignty primarily meant to benefit European citizens, or does it embrace a global responsibility? The empirical analysis then maps the EU Commission’s AI strategy since 2018 and the EU AI Act negotiations onto these three dimensions. It reveals that EU AI policy prioritises jurisdictional independence over citizens sovereignty, that it embraces a global AI race logic, and that it largely neglects its impact on people beyond the EU. This orientation of EU AI sovereignty resonates with and feeds into rising geo-economic tensions. But it would underestimate the importance of public policy to assume that it flowed naturally from technological transformations, rather than from political choices.

Improving the role of communities in participatory forest management through artificial intelligence: the case of Nairobi city park community forest association

The integration of artificial intelligence (AI) into participatory forest management (PFM) is emerging as a promising strategy for promoting sustainable forest management in developing countries. Using a case study approach from Kenya involving 85 respondents from the Nairobi City Park Community Forest Association, this study explored the potential for AI implementation in PFM to improve community roles in data acquisition and management. The study results show that the current data management system for executing community roles in PFM is inefficient, time-consuming, and susceptible to errors. However, there are substantial gains and opportunities in implementing community roles through AI. AI utilization could be fostered through the existing Information Communication Technology (ICT) resources such as smartphones for efficient and transparent data processes. Notably, 90% of respondents express confidence in AI’s potential to enhance PFM efficiency. Despite this optimism, 67.1% emphasize the necessity of a comprehensive AI policy that emphasizes continuous community engagement and adaptation of AI to local contexts.

Analyzing Ghana's Pharmacy Act, 1994 (Act 489) Regarding Quality Control and Negligence Liability Measures for Artificial Intelligence Pharmacy Systems

The objective of this systematic review was to assess the adequacy of current medication management in Ghana considering the risks posed by increased artificial intelligence (AI) automation in pharmacies worldwide A qualitative comparative approach was used despite reviewed the Ghana 1994 Pharmacy Act against recognition of AI challenges and international governance guidelines . The results revealed flaws in terms of quality prerequisites, transparency checklists and liability mechanisms developed for AI systems compared to existing regulations of the manual process. Outdated approaches to patient care that fail to ensure patient safety or address threats to the accuracy of recommendations from data collection biases and technical errors. Proposed changes include a requirement for usability testing before approving AI pharmacy deployments and the creation of a review board to review post-implementation systems for validity. Updating regulations to deal with modern equipment puts innovation and responsible regulation in the fast-paced healthcare industry. This study contributes significantly to preliminary research on AI policy readiness in the Ghanaian legal context, and suggests a feasible methodology for exploring qualitative differences for use in companies and countries competing for technology a disturbing, increasingly beyond the date code. Early government reform helps keep pace with the realities of adoption.

Evaluating if Ghana's Health Institutions and Facilities Act 2011 (Act 829) Sufficiently Addresses Medical Negligence Risks from Integration of Artificial Intelligence Systems

With artificial intelligence (AI) integrated increasingly to enhance personalized diagnosis and data-driven treatment recommendations, this analysis examines the legal sufficiency of Ghana’s Health Institutions and Facilities Act 2011 (Act 829) to address medical negligence risks from reliance on AI systems in clinical settings. The CREAC framework structures evaluating gaps where existing health regulations may lack clarity for emerging issues of accountability. Explanation contextualizes the probabilistic nature of AI inferences and how general principles of medical negligence could have ambiguous application currently if erroneous AI contributions result in patient harm. Application to a hypothetical scenario assesses if adequate protections for appropriate integration exist across developers, systems, healthcare facilities, and practitioners under applicable interpretations of existing laws. Finding liability rules insufficient absent targeted AI governance, conclusions recommend amending Act 829 in key areas to codify expectations for responsible innovation and prevent ambiguity in liability.
 This work carries scientific novelty as one of the first structured jurisdictional analyses internationally of healthcare AI accountability gaps through a legal lens. Practical significance lies in setting the stage for strengthening protections in Ghana through proposed statutory reforms that reduce uncertainty around this crucial area for quality care. The method and recommendations offer a model for modernizing medical negligence law and AI policy amidst ongoing digitization in healthcare worldwide.

The impact of AI in physics education: a comprehensive review from GCSE to university levels

With the rapid evolution of artificial intelligence (AI), its potential implications for higher education have become a focal point of interest. This study delves into the capabilities of AI in physics education and offers actionable AI policy recommendations. Using openAI’s flagship gpt-3.5-turbo large language model (LLM), we assessed its ability to answer 1337 physics exam questions spanning general certificate of secondary education (GCSE), A-Level, and introductory university curricula. We employed various AI prompting techniques: Zero Shot, in context learning, and confirmatory checking, which merges chain of thought reasoning with reflection. The proficiency of gpt-3.5-turbo varied across academic levels: it scored an average of 83.4% on GCSE, 63.8% on A-Level, and 37.4% on university-level questions, with an overall average of 59.9% using the most effective prompting technique. In a separate test, the LLM’s accuracy on 5000 mathematical operations was found to be 45.2%. When evaluated as a marking tool, the LLM’s concordance with human markers averaged at 50.8%, with notable inaccuracies in marking straightforward questions, like multiple-choice. Given these results, our recommendations underscore caution: while current LLMs can consistently perform well on physics questions at earlier educational stages, their efficacy diminishes with advanced content and complex calculations. LLM outputs often showcase novel methods not in the syllabus, excessive verbosity, and miscalculations in basic arithmetic. This suggests that at university, there’s no substantial threat from LLMs for non-invigilated physics questions. However, given the LLMs’ considerable proficiency in writing physics essays and coding abilities, non-invigilated examinations of these skills in physics are highly vulnerable to automated completion by LLMs. This vulnerability also extends to pysics questions pitched at lower academic levels. It is thus recommended that educators be transparent about LLM capabilities with their students, while emphasizing caution against overreliance on their output due to its tendency to sound plausible but be incorrect.