Intelligence Policy Research Articles

Mapping science in artificial intelligence policy development: formulation, trends, and influences

Abstract This research maps the evolution of artificial intelligence (AI) policy and its scientific underpinnings. First, we analyzed the global AI policy landscape using the Overton policy documents database, which comprises millions of policy documents. Findings reveal a substantial increase in AI policy documents since 2018, with the USA, European Union (EU), and intergovernmental organizations leading policy development efforts. We also analyzed the scientific articles referenced within these policies. The USA stood out as a central hub in the production and funding of AI research, with other Global North countries playing a notable role alongside China. The research cited in AI policy documents predominantly features journals with a high-impact factor, such as Nature and Science. This analysis aims to deepen the understanding of the AI policy landscape, offering insights for academics and policymakers and contributing to managing AI’s global governance.

Analyzing the influence of smart and digital manufacturing on cost stickiness: A study of U.S. manufacturing firms

In the digital economy, smart and digital manufacturing has become an essential tool for firms to increase their industrial intelligence in recent years. This research uses data from U.S. manufacturing firms from 2011 to 2021 to investigate the effect of smart and digital manufacturing and its mechanism on firm cost stickiness. The findings demonstrate that the adoption of smart and digital manufacturing negatively impacts a firm's cost stickiness, suggesting that the adoption of smart and digital manufacturing has a deterrent effect on cost stickiness. Mechanism test results show that smart and digital manufacturing primarily lowers the cost stickiness of businesses by enhancing resource allocation effectiveness and improving information processing capabilities. Smart and digital manufacturing has a significantly stronger inhibitory effect on cost stickiness compared to collaborative smart and digital manufacturing modes. According to the examination of heterogeneity variables, firms with higher staff knowledge levels and non-SOE status experience a more pronounced inhibitory effect of smart and digital manufacturing on their cost stickiness. The value effect analysis indicates that smart and digital manufacturing eventually increases a company's present and future performance, enhancing long-term value by reducing cost stickiness. This study offers guidance and insights for the development of firm operational strategies and the promotion of national industrial intelligence policies.

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.

Columns: Reimagining Research: Research for School Policy: Students Propose Guidelines for Artificial Intelligence

Amy Matthusen describes how she asked her students to help develop her school’s artificial intelligence policy through an extensive collaborative research project.

High Quality Development and Innovation of Current Financial Mechanism

The high quality finance innovation will be able to be proceeded according to the latest study in China by president with central political bureau this year of 2024. The main topic has indicated the risky and security for financial mechanism of enterprises where it is going to be discussed many times. For ensuring that it proceeded in these institutions the detailed methods enable to be narrated here for guaranteeing this one to do smoothly. The bank will be responsible for the entity one who is going to invest their loan for enlarging enterprise with macro controlling the policies and strategy support where they can instruct for better utilization and less risky for continuing the their investment. Tax national bureau should regulate some money to bank with privileged intelligence policy. Meanwhile, they monitor the money flow route and give to advise to them in terms of legislating procedure. For the sake of utilizing bilateral strategy policy commonly mutual beneficial one mechanism is going to be made. At the same time the stock market will enable financial innovation reformation to become high quality developing through stabilizing and creating the new stock exchange behaviour. It will be able to control and monitor the financial healthy developing under new innovation mechanism.

A quantitative study of disruptive technology policy texts: An example of China’s artificial intelligence policy

Abstract Purpose The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention. This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence (AI) disruptive technology policy, and to put forward suggestions for optimizing China’s AI disruptive technology policy. Design/methodology/approach Develop a three-dimensional analytical framework for “policy tools-policy actors-policy themes” and apply policy tools, social network analysis, and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools, cooperative relationships among policy actors, and the trends in policy theme settings within China’s innovative AI technology policy. Findings We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close. Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects, forming a “center-periphery” network structure. Policy tool usage is predominantly focused on supply and environmental types, with a severe inadequacy in demand-side policy tool utilization. Policy themes are diverse, encompassing topics such as “Intelligent Services” “Talent Cultivation” “Information Security” and “Technological Innovation”, which will remain focal points. Under the themes of “Intelligent Services” and “Intelligent Governance”, policy tool usage is relatively balanced, with close collaboration among policy entities. However, the theme of “AI Theoretical System” lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities. Research limitations The data sources and experimental scope are subject to certain limitations, potentially introducing biases and imperfections into the research results, necessitating further validation and refinement. Practical implications The study introduces a three-dimensional analysis framework for disruptive technology policy texts, which is significant for formulating and enhancing disruptive technology policies. Originality/value This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts. It systematically evaluates China’s AI policies quantitatively, focusing on policy tools, policy actors, policy themes. The study uncovers the characteristics and deficiencies of current AI policies, offering recommendations for formulating and enhancing disruptive technology policies.

CIL's New Generative AI Policy

The Editors-in-Chief of Communications in Information Literacy discuss the development of the journal's new generative artificial intelligence (AI) policy.

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.

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.

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.

An Ambitious Artificial Intelligence Policy in a Decentralised Governance System: Evidence From Indonesia

This study investigates Indonesia’s ambitious artificial intelligence (AI) policy within the context of its decentralised governance structure. Through in-depth case studies in Jakarta, Central Java, and East Java, we analyse emerging AI-based policy responses and their challenges in a rapidly evolving technological landscape. Drawing from elite interviews conducted with central and local government officials and documentary research, this study offers rare insights into the local perspective on the struggle to accommodate the central government's ambitious plan with limited resources. This article finds that the divergence in the views and visions of AI between central and local governments has complicated the formulation and implementation of AI-based policies. Central authorities wield a dominant role, evident through regulatory mandates and a centralised decision-making approach that can potentially constrain local autonomy. This power asymmetry, coupled with the lack of specific AI-focused regulations, challenges local governments’ capacity to independently design and manage AI initiatives aligned with their unique contexts. Interestingly, instead of showing their resistance towards the ambitious national plan, local leaders have embraced AI policies, positioning them as innovative tools to enhance popularity in the lead-up to the 2024 general election.

The innovation model and upgrade path of digitalization driven tourism industry: Longitudinal case study of OCT

Unlike the progressive growth of developed countries that have transitioned from large-scale tourism to intelligent tourism, China's tourism sector exhibits characteristics of short-term leapfrog strategic change. However, existing research lacks a sufficient theoretical explanation of how China's tourism industry can achieve this leapfrog strategic change. Based on a longitudinal case study, this study finds that digital supplementation and intelligent innovation constitute two key stages in Chinese enterprises' leap-forward strategic change from large-scale park tourism to intelligent tourism from 2012 to 2020. Digitalization is an important path for Chinese enterprises to realize transformation and upgrading, mainly through digital acceleration and reconstruction learning mechanisms, to drive the double leap in strategic change and tourism industry model innovation. In the context of a mismatch between digital technology and managerial skills, this study provides a theoretical model for firms to implement a leap-forward strategic change in intelligent tourism. It not only supports strategic change theory and organizational learning but also provides management illumination for digital rural construction, national tourism intelligence policy development, and enterprise intelligent tourism reform practice.

Artificial intelligence policies of the states under sanctions

The purpose of this study is to identify similar features and differences in the artificial intelligence (AI) policies of the states claiming global (Russia) and regional (Iran) influence. The context of this study is the sanctions of Western countries in relation to Russia and Iran. The study methodology is based on the principles of comparative analysis. We analyze legislation and strategic documents, as well as institutions and practices in the field of artificial intelligence development in these states. Identifying national AI policy patterns can be an important predictive tool. In order to conduct a comparative analysis of legislation, we developed a set of parameters for comparison: (1) AI development goals, (2) authorities responsible for the implementation of AI projects, (3) AI development priorities, (4) positions of countries in the global AI development rankings. The basis of the study is the analysis of AI development indices (Government AI Readiness 2021 by Oxford Insights, Nature Index 2020 Artificial Intelligence, and Global AI Index 2021 by Tortoise Media), official statistics, national legislation, and media publications.

Innovation to Impact: Implementing Artificial Intelligence (AI) Policies for Africa’s Development

Innovation to Impact: Implementing Artificial Intelligence (AI) Policies for Africa’s Development

Building a Business and Strategic Intelligence Policy as a Strategy for Promoting Congolese Business Progress and Healthy Economic Development in Eastern DRC

Building a Business and Strategic Intelligence Policy as a Strategy for Promoting Congolese Business Progress and Healthy Economic Development in Eastern DRC

Who will watch the watchers? The state of United States artificial intelligence policy and self-regulation in an ever-changing digital world.

As regulators seek to keep up with the sheer pace of tech change in the AI space, it is likely that we will see a shift from high-level principles to concrete public policy with the public sector first. However, as is to be expected, the idea of self-regulation in technology-by-technology companies poses the obvious issues minimally presenting an earnest conflict of interest within the very process of legislation. Influential tech company voices in discussions related to AI regulations can't be privileged over the rest of society's needs or concerns. AI is revolutionizing various aspects of society, including employment and political campaigns but it does pose certain risks. As a result, policymakers are in a hurry to safeguard the public from AI-related risks while ensuring that innovation remains unhampered. The need to foresee additional regulation in this space is important for deploying AI in operations in all industries. The AI community, specifically, ought to adopt a proactive approach in advocating for the responsible and intelligent utilization of these technologies.

Off‐policy correction algorithm for double Q network based on deep reinforcement learning

AbstractA deep reinforcement learning (DRL) method based on the deep deterministic policy gradient (DDPG) algorithm is proposed to address the problems of a mismatch between the needed training samples and the actual training samples during the training of intelligence, the overestimation and underestimation of the existence of Q‐values, and the insufficient dynamism of the intelligence policy exploration. This method introduces the Actor‐Critic Off‐Policy Correction (AC‐Off‐POC) reinforcement learning framework and an improved double Q‐value learning method, which enables the value function network in the target task to provide a more accurate evaluation of the policy network and converge to the optimal policy more quickly and stably to obtain higher value returns. The method is applied to multiple MuJoCo tasks on the Open AI Gym simulation platform. The experimental results show that it is better than the DDPG algorithm based solely on the different policy correction framework (AC‐Off‐POC) and the conventional DRL algorithm. The value of returns and stability of the double‐Q‐network off‐policy correction algorithm for the deep deterministic policy gradient (DCAOP‐DDPG) proposed by the authors are significantly higher than those of other DRL algorithms.

Radical Uncertainty and Artificial Intelligence policy issues for Aotearoa New Zealand

This article explores the intricacies of policymaking in the 21st century, with a focus on Aotearoa New Zealand’s approach to artificial intelligence (AI). The analysis underscores the challenges presented by risk, uncertainty, and especially radical (fundamental) uncertainty, which complicates the formulation of robust AI policies. Using Aotearoa New Zealand as a case study, the article delves into the multifaceted policy challenges AI presents, emphasising the need for adaptive policymaking, stakeholder engagement, a precautionary approach and ethical considerations. In short, a balanced interplay of evidence, values and power in the policymaking process is required.

Agenda-setting in nascent policy subsystems: issue and instrument priorities across venues

The “policy subsystem” has long been a key concept in our understanding of how policies on a given topic are produced. However, we know much less about policymaking in nascent policy subsystems. This article draws on the theories of agenda-setting and venue shopping to argue that the similarity and convergence of policy subsystems’ agendas across different institutional venues and over time are features that distinguish more nascent policy subsystems from their more established, mature counterparts. In simple terms, policy venues’ agendas converge when policy actors begin to discuss the same issues and instruments instead of talking past one another. The article illustrates this argument using textual data on Germany’s emerging Artificial Intelligence (AI) policy: print media debates, parliamentary debates, and a government consultation from the period between November 2017 and November 2019. The insights from our analysis show that actors emphasize somewhat different policy issues and instruments related to AI in different venues. Nevertheless, the longitudinal analysis suggests that the debate does seem to converge across different venues, which indicates the formation of a subsystem-specific policy agenda regarding AI.